Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A data scientist is training a credit risk model and wants to use Azure Machine Learning's Responsible AI dashboard to identify if the model is biased against a certain demographic group. Which component of the dashboard should they use to evaluate this?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Model Interpretability
Model Interpretability provides explanations for individual predictions but does not directly assess fairness or bias across groups.
B
Best answer
Model Fairness Assessment
This component analyzes model predictions across predefined sensitive groups to identify and measure unfair bias.
C
Distractor review
Error Analysis
Error Analysis helps identify high-error data slices but does not inherently focus on demographic bias.
D
Distractor review
Data Balance Analysis
Data Balance Analysis checks for imbalances in the training data, which could lead to bias, but does not evaluate actual model predictions for fairness.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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Question 5
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Question 6
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FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Model Fairness Assessment — The Responsible AI dashboard includes multiple components. The Model Fairness Assessment component specifically evaluates disparities in model predictions across sensitive groups (e.g., gender, ethnicity) to detect bias. Model Interpretability helps explain why predictions are made but does not directly measure bias. Error Analysis identifies data slices where the model makes more errors but does not specifically focus on demographic bias. Data Balance Analysis examines the distribution of features in the dataset but does not measure model bias; it can indicate potential data imbalances that might lead to bias but does not evaluate the model's outputs. Therefore, the correct component for bias evaluation is Model Fairness Assessment.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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