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Question 1/101/10
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the
application.
Which action will meet this requirement with the LEAST operational overhead?
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the
application.
Which action will meet this requirement with the LEAST operational overhead?
Select the answer:Select the answer
1 correct answerA.
Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
B.
Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
C.
Use the SageMaker Model Registry and model groups to catalog the models.
D.
Use the SageMaker Model Registry and unique tags for each model version.
Quiz
Question 2/102/10
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?
Select the answer:Select the answer
1 correct answerA.
Use Managed Spot Training.
B.
Use SageMaker managed warm pools.
C.
Use SageMaker Training Compiler.
D.
Use the SageMaker distributed data parallelism (SMDDP) library.
Quiz
Question 3/103/10
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved
models can be deployed to production endpoints.
Which solution will meet this requirement?
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved
models can be deployed to production endpoints.
Which solution will meet this requirement?
Select the answer:Select the answer
1 correct answerA.
Use SageMaker Experiments to facilitate the approval process during model registration.
B.
Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
C.
Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
D.
Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
Quiz
Question 4/104/10
Case Study
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are
deployed to real-time endpoints from the application.
Which action will meet this requirement?
A company is building a web-based AI application by using Amazon SageMaker. The application will
provide the following capabilities and features: ML experimentation, training, a
central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The
training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are
deployed to real-time endpoints from the application.
Which action will meet this requirement?
Select the answer:Select the answer
1 correct answerA.
Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
B.
Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
C.
Use AWS Glue Data Quality to monitor bias.
D.
Use SageMaker notebooks to compare the bias.
Quiz
Question 5/105/10
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the
.csv files are populated. The columns are not labeled. An ML
engineer needs to prepare and store the data so that the company can use the data to train ML
models.
Select and order the correct steps from the following list to perform this task. Each step should be
selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.
.csv files are populated. The columns are not labeled. An ML
engineer needs to prepare and store the data so that the company can use the data to train ML
models.
Select and order the correct steps from the following list to perform this task. Each step should be
selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.
Check the image below to see the right answer:Select the answer
1 correct answerStep 1: Use AWS Glue crawlers to infer the schemas and available columns.
Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.
Step 3: Store the resulting data back in Amazon S3.
Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
Why? The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw
data in Amazon S3 to automatically infer the schema, including available columns, data types, and
any missing or incomplete entries.
How? Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the
crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can
then be used for subsequent transformations.
Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
Why? Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and
transformation of data. It supports imputation of missing values, renaming columns, feature
engineering, and more without requiring extensive coding.
How? Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning
and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and
creating derived features.
Step 3: Store the Resulting Data Back in Amazon S3
Why? After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be
used for training machine learning models.
How? Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This
ensures the processed data is readily accessible for ML workflows.
Order Summary:
Use AWS Glue crawlers to infer schemas and available columns.
Use AWS Glue DataBrew for data cleaning and feature engineering.
Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging
AWS services for automation and scalability.
Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.
Step 3: Store the resulting data back in Amazon S3.
Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
Why? The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw
data in Amazon S3 to automatically infer the schema, including available columns, data types, and
any missing or incomplete entries.
How? Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the
crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can
then be used for subsequent transformations.
Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
Why? Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and
transformation of data. It supports imputation of missing values, renaming columns, feature
engineering, and more without requiring extensive coding.
How? Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning
and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and
creating derived features.
Step 3: Store the Resulting Data Back in Amazon S3
Why? After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be
used for training machine learning models.
How? Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This
ensures the processed data is readily accessible for ML workflows.
Order Summary:
Use AWS Glue crawlers to infer schemas and available columns.
Use AWS Glue DataBrew for data cleaning and feature engineering.
Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging
AWS services for automation and scalability.
Quiz
Question 6/106/10
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to
train a model.
Select and order the steps from the following list to create and use the features in Feature Store.
Each step should be selected one time. (Select and order three.)
• Access the store to build datasets for training.
• Create a feature group.
• Ingest the records.
train a model.
Select and order the steps from the following list to create and use the features in Feature Store.
Each step should be selected one time. (Select and order three.)
• Access the store to build datasets for training.
• Create a feature group.
• Ingest the records.
Check the image below to see the right answer:Select the answer
1 correct answer
Step 1: Create a feature group.
Step 2: Ingest the records.
Step 3: Access the store to build datasets for training.
Step 1: Create a Feature Group
Why? A feature group is the foundational unit in SageMaker Feature Store, where features are
defined, stored, and organized. Creating a feature group specifies the schema (name, data type) for
the features and the primary keys for data identification.
How? Use the SageMaker Python SDK or AWS CLI to define the feature group by specifying its name,
schema, and S3 storage location for ofline access.
Step 2: Ingest the Records
Why? After creating the feature group, the raw data must be ingested into the Feature Store. This
step populates the feature group with data, making it available for both real-time and ofline use.
How? Use the SageMaker SDK or AWS CLI to batch-ingest historical data or stream new records into
the feature group. Ensure the records conform to the feature group schema.
Step 3: Access the Store to Build Datasets for Training
Why? Once the features are stored, they can be accessed to create training datasets. These datasets
combine relevant features into a single format for machine learning model training.
How? Use the SageMaker Python SDK to query the ofline store or retrieve real-time features using
the online store API. The ofline store is typically used for batch training, while the online store is
used for inference.
Order Summary:
Create a feature group.
Ingest the records.
Access the store to build datasets for training.
This process ensures the features are properly managed, ingested, and accessible for model training
using Amazon SageMaker Feature Store.
Quiz
Question 7/107/10
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a
continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the
model. The pipeline must run automatically when new training data for the model is uploaded to an
Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one
time or not at all. (Select and order three.)
• An S3 event notification invokes the pipeline when new data is uploaded.
• S3 Lifecycle rule invokes the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
• The pipeline deploys the model to SageMaker Model Registry.
continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the
model. The pipeline must run automatically when new training data for the model is uploaded to an
Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one
time or not at all. (Select and order three.)
• An S3 event notification invokes the pipeline when new data is uploaded.
• S3 Lifecycle rule invokes the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
• The pipeline deploys the model to SageMaker Model Registry.
Check the image below to see the right answer:Select the answer
1 correct answerStep 1: An S3 event notification invokes the pipeline when new data is uploaded.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.
Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded
to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda,
which can then invoke AWS CodePipeline.
How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda,
which in turn triggers the CodePipeline.
Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
Why? The uploaded data is used to retrain the ML model to incorporate new information and
maintain performance. This step is critical to updating the model with fresh data.
How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the
S3 bucket and retrains the model.
Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it
available for real-time inference.
How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the
SageMaker endpoint with the retrained model.
Order Summary:
An S3 event notification invokes the pipeline when new data is uploaded.
SageMaker retrains the model by using the data in the S3 bucket.
The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous
retraining and deployment of the ML model in Amazon SageMaker.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.
Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded
Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded
to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda,
which can then invoke AWS CodePipeline.
How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda,
which in turn triggers the CodePipeline.
Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket
Why? The uploaded data is used to retrain the ML model to incorporate new information and
maintain performance. This step is critical to updating the model with fresh data.
How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the
S3 bucket and retrains the model.
Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it
available for real-time inference.
How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the
SageMaker endpoint with the retrained model.
Order Summary:
An S3 event notification invokes the pipeline when new data is uploaded.
SageMaker retrains the model by using the data in the S3 bucket.
The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous
retraining and deployment of the ML model in Amazon SageMaker.
Quiz
Question 8/108/10
An ML engineer is building a generative AI application on Amazon Bedrock by using large language
models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should
be selected one time or not at all. (Select three.)
• Embedding
• Retrieval Augmented Generation (RAG)
• Temperature
• Token
models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should
be selected one time or not at all. (Select three.)
• Embedding
• Retrieval Augmented Generation (RAG)
• Temperature
• Token
Check the image below to see the right answer:Select the answer
1 correct answer
Text representation of basic units of data processed by LLMs: Token
High-dimensional vectors that contain the semantic meaning of text: Embedding
Enrichment of information from additional data sources to improve a generated response: Retrieval
Augmented Generation (RAG)
Comprehensive Detailed Explanation
Token:
Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM
processes. For example, "running" might be split into two tokens: "run" and "ing."
Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that
the model can understand and generate text efficiently.
Embedding:
Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are
representations of words, sentences, or even paragraphs in a way that reflects their relationships and
meaning.
Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring
semantic understanding. They allow the model to "understand" text contextually.
Retrieval Augmented Generation (RAG):
Description: A technique where information is enriched or retrieved from external data sources (e.g.,
knowledge bases or document stores) to improve the accuracy and relevance of a model's generated
responses.
Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and
up-to-date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage
these concepts to build robust and contextually aware generative AI applications on Amazon
Bedrock.
Quiz
Question 9/109/10
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model
will base predictions on several features The ML engineer will use the following feature engineering
techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature
engineering technique should be selected one time or not at all (Select three.)
will base predictions on several features The ML engineer will use the following feature engineering
techniques to estimate the prices of the homes:
• Feature splitting
• Logarithmic transformation
• One-hot encoding
• Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature
engineering technique should be selected one time or not at all (Select three.)
Check the image below to see the right answer:Select the answer
1 correct answerCity (name): One-hot encoding
Type_year (type of home and year the home was built): Feature splitting
Size of the building (square feet or square meters): Standardized distribution
City (name): One-hot encoding
Why? The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it
into a numeric format. This encoding creates binary columns for each unique category (e.g., cities
like "New York" or "Los Angeles"), which the model can interpret.
Type_year (type of home and year the home was built): Feature splitting
Why? "Type_year" combines two pieces of information into one column, which could confuse the
model. Feature splitting separates this column into two distinct features: "Type of home" and "Year
built," enabling the model to process each feature independently.
Size of the building (square feet or square meters): Standardized distribution
Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a
mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other
features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is
correctly formatted and optimized for the model to make accurate predictions.
Type_year (type of home and year the home was built): Feature splitting
Size of the building (square feet or square meters): Standardized distribution
City (name): One-hot encoding
Why? The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it
into a numeric format. This encoding creates binary columns for each unique category (e.g., cities
like "New York" or "Los Angeles"), which the model can interpret.
Type_year (type of home and year the home was built): Feature splitting
Why? "Type_year" combines two pieces of information into one column, which could confuse the
model. Feature splitting separates this column into two distinct features: "Type of home" and "Year
built," enabling the model to process each feature independently.
Size of the building (square feet or square meters): Standardized distribution
Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a
mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other
features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is
correctly formatted and optimized for the model to make accurate predictions.
Quiz
Question 10/1010/10
Case study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes
transaction logs, customer profiles, and tables from an on-premises MySQL database. The
transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally,
many of the features have interdependencies. The algorithm is not capturing all the desired
underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?
An ML engineer is developing a fraud detection model on AWS. The training dataset includes
transaction logs, customer profiles, and tables from an on-premises MySQL database. The
transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally,
many of the features have interdependencies. The algorithm is not capturing all the desired
underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?
Select the answer:Select the answer
1 correct answerA.
Amazon EMR Spark jobs
B.
Amazon Kinesis Data Streams
C.
Amazon DynamoDB
D.
AWS Lake Formation
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