The answer is Transparency. This principle is most directly violated because the AI system cannot explain its resume decisions, meaning the organization lacks any insight into which features influenced the outcomes or how the model arrived at those rejections. Microsoft’s Transparency principle demands that AI systems be understandable and that their decisions be explainable, which is precisely what is missing here—the system operates as an opaque “black box” with no traceable reasoning. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your grasp of how Transparency ties to explainability in responsible AI, often appearing as a trap where learners confuse it with Fairness or Accountability. A common memory tip: if you cannot see the “how” or “why” behind a decision, think “Transparency = X-ray vision” for the model’s inner workings.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
Why wrong: While the system may be biased, the primary issue described is the lack of understanding of the decision process, not bias detection or mitigation.
B
Accountability
Why wrong: Accountability is relevant but the most direct violation is the inability to explain the model, which falls under transparency.
C
Transparency
Transparency means AI systems should be understandable, and operators should be able to explain decisions. The company's inability to explain the rejection directly violates this principle.
D
Privacy and security
Why wrong: Privacy and security concern data protection, not the explainability of AI decisions.
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.
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
While the system may be biased, the primary issue described is the lack of understanding of the decision process, not bias detection or mitigation.
✗
Accountability
Why it's wrong here
Accountability is relevant but the most direct violation is the inability to explain the model, which falls under transparency.
✓
Transparency
Why this is correct
Transparency means AI systems should be understandable, and operators should be able to explain decisions. The company's inability to explain the rejection directly violates this principle.
Related concept
Read the scenario before looking for a memorised answer.
✗
Privacy and security
Why it's wrong here
Privacy and security concern data protection, not the explainability of AI decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
In practice, transparency is often achieved through techniques like feature importance analysis (e.g., SHAP or LIME) or using inherently interpretable models (e.g., decision trees or linear models). For deep learning or ensemble models, post-hoc explanation methods can approximate which input features most influenced a prediction. Without such mechanisms, organizations cannot audit decisions, debug model behavior, or comply with 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 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.
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 — 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.
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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