AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A university deploys an AI model to predict which students are at risk of dropping out. The predictions are used to offer targeted support. Students who may be negatively impacted by this prediction have the right to understand how the model arrived at its decision. Which Microsoft responsible AI principle is most directly relevant?
Why wrong: Fairness is concerned with ensuring the system does not discriminate against groups, but the scenario focuses on understanding the decision process, not bias.
B
Reliability and safety
Why wrong: Reliability and safety refer to the system performing correctly and safely under expected conditions, not explainability.
C
Transparency
Transparency requires that AI systems be understandable and that the basis of their decisions be communicated to affected individuals, which directly addresses the need to explain the prediction.
D
Privacy and security
Why wrong: Privacy and security involve protecting personal data, not explaining model decisions.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transparency
Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, students have the right to know how the model arrived at its dropout prediction, which directly aligns with transparency's goal of providing clear explanations for AI decisions. This principle ensures that affected individuals can access meaningful information about the logic and factors used by the model.
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.
✗
Fairness
Why it's wrong here
Fairness is concerned with ensuring the system does not discriminate against groups, but the scenario focuses on understanding the decision process, not bias.
✗
Reliability and safety
Why it's wrong here
Reliability and safety refer to the system performing correctly and safely under expected conditions, not explainability.
✓
Transparency
Why this is correct
Transparency requires that AI systems be understandable and that the basis of their decisions be communicated to affected individuals, which directly addresses the need to explain the prediction.
Related concept
Read the scenario before looking for a memorised answer.
✗
Privacy and security
Why it's wrong here
Privacy and security involve protecting personal data, not explaining model decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between transparency (explaining how a decision was made) and fairness (ensuring no bias), causing candidates to mistakenly select fairness when the question is about understanding model reasoning.
Trap categories for this question
Scenario analysis trap
Fairness is concerned with ensuring the system does not discriminate against groups, but the scenario focuses on understanding the decision process, not bias.
Detailed technical explanation
How to think about this question
Transparency in AI often involves implementing interpretability techniques such as SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to generate feature importance scores for individual predictions. In Azure Machine Learning, the 'Model Interpretability' SDK can produce global and local explanations, showing which features (e.g., attendance rate, past grades) most influenced a specific dropout risk score. This is critical in educational settings where students may challenge or appeal automated decisions under data protection regulations like GDPR's right to explanation.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Transparency — Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, students have the right to know how the model arrived at its dropout prediction, which directly aligns with transparency's goal of providing clear explanations for AI decisions. This principle ensures that affected individuals can access meaningful information about the logic and factors used by the model.
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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Question Discussion
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