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AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

A company deploys an AI system to screen job resumes. The system consistently rejects candidates from a certain university, but the company cannot determine which features led to the decision or how the model arrived at that outcome. Which Microsoft responsible AI principle is most directly violated?

⚠ Common exam trap

Microsoft often tests the distinction between Fairness (bias) and Transparency (explainability), so the trap here is that candidates see a potentially biased outcome and immediately choose Fairness, missing that the core violation is the lack of explainability, not the bias itself.

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

Transparency

The scenario describes a system that makes decisions without providing any insight into how or why those decisions were made. Transparency, as a Microsoft responsible AI principle, requires that AI systems be understandable and that their decisions can be explained. Since the company cannot determine which features led to the rejection or how the model arrived at the outcome, the lack of explainability directly violates the Transparency principle.

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

    A fairness violation would require evidence that the model's screening decisions systematically disadvantage protected groups, such as by rejecting candidates of a particular ethnicity at a higher rate. The scenario instead centers on the operator's inability to articulate why a specific applicant was rejected, which is an explainability concern. Without access to the decision logic, one cannot even determine whether bias exists, so fairness is not the immediate principle breached.

  • Accountability

    Why it's wrong here

    Accountability is about establishing which person or team owns the model and its outcomes, including maintaining audit trails and taking corrective action. Although an AI developer or HR manager should ultimately be answerable for the resume screener, the described failure is a lack of explanation for the model's judgment, not a lack of ownership. Thus, accountability is relevant to governance but does not pinpoint the transparency deficiency at issue.

  • Transparency

    Why this is correct

    Transparency is the principle that AI systems should be interpretable and that operators must be able to provide understandable reasons for automated decisions. Because the company cannot explain why the resume-screening system rejected a candidate, the decision is effectively a black-box action. This directly violates the AI-900 expectation that AI systems be open about their logic and behavior, making transparency the correct answer.

  • Privacy and security

    Why it's wrong here

    Privacy and security govern how resume data is collected, stored, accessed, and protected from breaches or unauthorized use. Nothing in the scenario suggests that candidate information was exposed, misused, or handled without consent, so a data-protection violation is not present. The central problem remains the inability to explain a decision, which is unrelated to confidentiality or infrastructure safeguards.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.