Demo Microsoft AI-900 Exam Questions

Demo practice questions for guest users.

Section: Practice Mode 4 Questions
Demo Practice
Question 1

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?

Correct Answer: B
Explanation:
Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency
and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with
regulations and best practices. One key aspect of this is understanding the relationship between
input variables (features) and model output. Knowing both the magnitude and direction of the
impact each feature (feature importance) has on the predicted value helps better understand and
explain the model. With model explain ability, we enable you to understand feature importance as
part of automated ML runs.
Question 2

HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Correct Answer: A
Explanation:

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.
Question 3

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?

Correct Answer: B
Explanation:
Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency
and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with
regulations and best practices. One key aspect of this is understanding the relationship between
input variables (features) and model output. Knowing both the magnitude and direction of the
impact each feature (feature importance) has on the predicted value helps better understand and
explain the model. With model explain ability, we enable you to understand feature importance as
part of automated ML runs.

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