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Quiz
Question 1/101/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
Which normalization type should you use?
Select the answer:Select the answer
1 correct answerA.
Streaming
B.
Weight
C.
Batch
D.
Cosine
Quiz
Question 2/102/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to resolve the local machine learning pipeline performance issue. What should you do?
Select the answer:Select the answer
1 correct answerA.
Increase Graphic Processing Units (GPUs).
B.
Increase the learning rate.
C.
Increase the training iterations,
D.
Increase Central Processing Units (CPUs).
Quiz
Question 3/103/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
DRAG DROP
You need to modify the inputs for the global penalty event model to address the bias and variance
issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
You need to modify the inputs for the global penalty event model to address the bias and variance
issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 4/104/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
Which environment should you use?
Select the answer:Select the answer
1 correct answerA.
Azure HDInsight with Spark MLlib
B.
Azure Cognitive Services
C.
Azure Machine Learning Studio
D.
Microsoft Machine Learning Server
Quiz
Question 5/105/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
DRAG DROP
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 6/106/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
DRAG DROP
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 7/107/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
DRAG DROP
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from
the list of actions to the answer area and arrange them in the correct order.
Check the image below to see the right answer:Select the answer
1 correct answer
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance
In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification
model that assigns to observations the label of the class of training samples whose mean (centroid)
is closest to the observation.
Reference:
[https://en.wikipedia.org/wiki/Nearest_centroid_classifier](https://en.wikipedia.org/wiki/Nearest_centroid_classifier)
[https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-](https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-)
clustering
Quiz
Question 8/108/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the
answer area.
NOTE: Each correct selection is worth one point.
How should you complete the code segment? To answer, select the appropriate options in the
answer area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Quiz
Question 9/109/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to use the Python language to build a sampling strategy for the global penalty detection
models.
How should you complete the code segment? To answer, select the appropriate options in the
answer area.
NOTE: Each correct selection is worth one point.
models.
How should you complete the code segment? To answer, select the appropriate options in the
answer area.
NOTE: Each correct selection is worth one point.
Check the image below to see the right answer:Select the answer
1 correct answer
Box 1: import pytorch as deeplearninglib
Box 2: ..DistributedSampler(Sampler)..
DistributedSampler(Sampler):
Sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such
case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a
subset of the original dataset that is exclusive to it.
Scenario: Sampling must guarantee mutual and collective exclusively between local and global
segmentation models that share the same features.
Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10)
Incorrect Answers: ..SGD..
Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent
(SGD) are running too slow.
Box 4: .. nn.parallel.DistributedDataParallel..
DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset.
It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.
Reference:
[https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py](https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py)
Quiz
Question 10/1010/10
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
Overview
You are a data scientist in a company that provides data science for professional sporting events.
Models will be global and local market data to meet the following business goals:
• Understand sentiment of mobile device users at sporting events based on audio from crowd
reactions.
• Access a user's tendency to respond to an advertisement.
• Customize styles of ads served on mobile devices.
• Use video to detect penalty events.
Current environment
Requirements
• Media used for penalty event detection will be provided by consumer devices. Media may include
images and videos captured during the sporting event and snared using social media. The images
and videos will have varying sizes and formats.
• The data available for model building comprises of seven years of sporting event media. The
sporting event media includes: recorded videos, transcripts of radio commentary, and logs from
related social media feeds feeds captured during the sporting evens.
• Crowd sentiment will include audio recordings submitted by event attendees in both mono and
stereo
Formats.
Advertisements
• Ad response models must be trained at the beginning of each event and applied during the
sporting event.
• Market segmentation nxxlels must optimize for similar ad resporr.r history.
• Sampling must guarantee mutual and collective exclusivity local and global segmentation models
that share the same features.
• Local market segmentation models will be applied before determining a user’s propensity to
respond to an advertisement.
• Data scientists must be able to detect model degradation and decay.
• Ad response models must support non linear boundaries features.
• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa
deviates from 0.1 +/-5%.
• The ad propensity model uses cost factors shown in the following diagram:
The ad propensity model uses proposed cost factors shown in the following diagram:
Performance curves of current and proposed cost factor scenarios are shown in the following
diagram:
Penalty detection and sentiment
Findings
• Data scientists must build an intelligent solution by using multiple machine learning models for
penalty event detection.
• Data scientists must build notebooks in a local environment using automatic feature engineering
and model building in machine learning pipelines.
• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation
• Notebooks must execute with the same code on new Spark instances to recode only the source of
the data.
• Global penalty detection models must be trained by using dynamic runtime graph computation
during training.
• Local penalty detection models must be written by using BrainScript.
• Experiments for local crowd sentiment models must combine local penalty detection data.
• Crowd sentiment models must identify known sounds such as cheers and known catch phrases.
Individual crowd sentiment models will detect similar sounds.
• All shared features for local models are continuous variables.
• Shared features must use double precision. Subsequent layers must have aggregate running mean
and standard deviation metrics Available.
segments
During the initial weeks in production, the following was observed:
• Ad response rates declined.
• Drops were not consistent across ad styles.
• The distribution of features across training and production data are not consistent.
Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that
come from location sources are being used as raw features. A suggested experiment to remedy the
bias and variance issue is to engineer 10 linearly uncorrected features.
Penalty detection and sentiment
• Initial data discovery shows a wide range of densities of target states in training data used for crowd
sentiment models.
• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are
running too stow.
• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on
region.
• The performance of the global penalty detection models show lower variance but higher bias when
comparing training and validation sets. Before implementing any feature changes, you must confirm
the bias and variance using all training and validation cases.
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
What should you do?
Select the answer:Select the answer
1 correct answerA.
Apply an analysis of variance (ANOVA).
B.
Apply a Pearson correlation coefficient.
C.
Apply a Spearman correlation coefficient.
D.
Apply a linear discriminant analysis.
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