Quiz about Certification Exam, Microsoft, Microsoft Certified: Azure AI Fundamentals

Question 1

For a machine learning progress, how should you split data for training and evaluation?

Accepted Answer

Randomly split the data into rows for training and rows for evaluation.

Answer

Use features for training and labels for evaluation.

Answer

Use labels for training and features for evaluation.

Answer

Randomly split the data into columns for training and columns for evaluation.

Question 2

A company employs a team of customer service agents to provide telephone and email support to
customers.

The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?

Accepted Answer

a reduced workload for the customer service agents

Answer

increased sales

Answer

improved product reliability

Question 3

You are developing a model to predict events by using classification.

You have a confusion matrix for the model scored on test data as shown in the following exhibit.

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Use the drop-down menus to select the answer choice that completes each statement based on the
information presented in the graphic.
NOTE: Each correct selection is worth one point.
Certification Exam Microsoft Microsoft Certified: Azure AI Fundamentals Microsoft-AI-900 2-3427195971

Certification Exam Microsoft Microsoft Certified: Azure AI Fundamentals Microsoft-AI-900 2-3427195971

Accepted Answer

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Box 1: 11

Certification Exam Microsoft Microsoft Certified: Azure AI Fundamentals Microsoft-AI-900 3-1123474080

TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as
positive or negative. The positive and negative instances that a classifier predicts correctly are called
true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances
are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in
this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case
The confusion matrix shows cases where both the predicted and actual values were 1 (known as true
positives) at the top left, and cases where both the predicted and the actual values were 0 (true
negatives) at the bottom right. The other cells show cases where the predicted and actual values
differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-
learning-designer/evaluate-model

Question 4

You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?

Accepted Answer

Enable Explain best model.

Answer

Set Validation type to Auto.

Answer

Set Primary metric to accuracy.

Answer

Set Max concurrent iterations to 0.

Question 5

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

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Accepted Answer

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Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-
detection

Question 6

To complete the sentence, select the appropriate option in the answer area.

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Accepted Answer

Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
“To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal
circumstances and in unexpected conditions. These systems should be able to operate as they were
originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It's
also important to be able to verify that these systems are behaving as intended under actual
operating conditions. How they behave and the variety of conditions they can handle reliably and
safely largely reflects the range of situations and circumstances that developers anticipate during
design and testing. We believe that rigorous testing is essential during system development and
deployment to ensure AI systems can respond safely in unanticipated situations and edge cases,
don't have unexpected performance failures, and don't evolve in ways that are inconsistent with
original expectations”

Question 7

DRAG DROP
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the
right. Each workload type may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.

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Accepted Answer

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Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection,
language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-
language-processing

Question 8

You are designing an AI system that empowers everyone, including people who have hearing, visual,
and other impairments.

This is an example of which Microsoft guiding principle for responsible AI?

Accepted Answer

inclusiveness

Answer

fairness

Answer

reliability and safety

Answer

accountability

Question 9

DRAG DROP
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right.
Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
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Accepted Answer

Box 1: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal
circumstances and in unexpected conditions. These systems should be able to operate as they were
originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: accountability
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business
information is becoming more critical and complex. With AI, privacy and data security issues require
especially close attention because access to data is essential for AI systems to make accurate and
informed predictions and decisions about people. AI systems must comply with privacy laws that
require transparency about the collection, use, and storage of data and mandate that consumers
have appropriate controls to choose how their data is used
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Question 10

To complete the sentence, select the appropriate option in the answer area.

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Accepted Answer

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Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and
consistently under normal circumstances and in unexpected conditions. These systems should be
able to operate as they were originally designed, respond safely to unanticipated conditions, and
resist harmful manipulation.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
AI systems should perform reliably and safely. For example, consider an AI-based software system for
an autonomous vehicle; or a machine learning model that diagnoses patient symptoms and
recommends prescriptions. Unreliability in these kinds of system can result in substantial risk to
human life.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-
responsible-ai

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