- A
Explaining the model's predictions using a simplified version of the model that is easier to interpret
Why wrong: Approximating with simpler models is LIME — SHAP values explain predictions by calculating each feature's exact mathematical contribution.
- B
Calculating each feature's contribution to a specific prediction to explain why the model made that decision
SHAP values quantify each feature's impact on each prediction — providing mathematically rigorous local and global explanations.
- C
Displaying the model's source code so users can verify what computations are performed
Why wrong: Code transparency is open-source model sharing — SHAP explains predictions through feature contributions, not code inspection.
- D
Testing the model on a separate evaluation dataset to report overall accuracy
Why wrong: Evaluation on test data measures overall performance — SHAP provides per-prediction explanations of feature contributions.
Quick Answer
The correct answer is calculating each feature’s contribution to a specific prediction to explain why the model made that decision. SHAP values, rooted in game theory, decompose a model’s output by assigning each input feature a numerical importance for a single prediction, offering a local explanation rather than a global overview. On the AI-900 exam, this concept tests your understanding of model interpretability in Azure Machine Learning, often appearing as a scenario where you must distinguish local explanations (like SHAP) from global feature importance or model simplification. A common trap is confusing SHAP with permutation importance, which measures overall feature impact across all predictions. Remember the memory tip: SHAP tells you “why this one prediction, not why the model in general.”
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'model explainability' using SHAP values in Azure Machine Learning?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Calculating each feature's contribution to a specific prediction to explain why the model made that decision
SHAP (SHapley Additive exPlanations) values are a game-theoretic approach that assigns each feature an importance value for a particular prediction. Option B is correct because SHAP values quantify the contribution of each input feature to the model's output, providing a local explanation for why a specific decision was made. This is distinct from global feature importance or model simplification.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Explaining the model's predictions using a simplified version of the model that is easier to interpret
Why it's wrong here
Approximating with simpler models is LIME — SHAP values explain predictions by calculating each feature's exact mathematical contribution.
- ✓
Calculating each feature's contribution to a specific prediction to explain why the model made that decision
Why this is correct
SHAP values quantify each feature's impact on each prediction — providing mathematically rigorous local and global explanations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Displaying the model's source code so users can verify what computations are performed
Why it's wrong here
Code transparency is open-source model sharing — SHAP explains predictions through feature contributions, not code inspection.
- ✗
Testing the model on a separate evaluation dataset to report overall accuracy
Why it's wrong here
Evaluation on test data measures overall performance — SHAP provides per-prediction explanations of feature contributions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse model explainability with model evaluation or model simplification, leading them to select Option A (surrogate model) or Option D (accuracy reporting) instead of recognizing that SHAP specifically provides per-feature contribution explanations for individual predictions.
Detailed technical explanation
How to think about this question
SHAP values are based on Shapley values from cooperative game theory, where each feature is treated as a 'player' and the prediction is the 'payout'. The method computes the average marginal contribution of a feature across all possible feature subsets, ensuring consistency and local accuracy. In Azure Machine Learning, the `interpret-community` package provides a SHAP explainer that can be used with any model, including deep neural networks, to generate local explanations for tabular, text, or image data.
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.
TExam Day Tips
- 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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Calculating each feature's contribution to a specific prediction to explain why the model made that decision — SHAP (SHapley Additive exPlanations) values are a game-theoretic approach that assigns each feature an importance value for a particular prediction. Option B is correct because SHAP values quantify the contribution of each input feature to the model's output, providing a local explanation for why a specific decision was made. This is distinct from global feature importance or model simplification.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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