The answer is transparency, because the development team’s inability to explain the neural network’s decision directly violates Microsoft’s responsible AI principle that systems must be understandable and interpretable. This scenario illustrates the core tension between the transparency principle and a black box model: complex neural networks learn patterns from historical data but often produce decisions that are opaque, making it impossible to provide a meaningful explanation to a rejected candidate. On the AI-900 exam, this concept tests your understanding that transparency requires explainable AI decisions, not just accurate outputs—a common trap is confusing transparency with fairness or reliability. Remember the memory tip: “If you can’t explain it, you’ve lost transparency”—always link black box models to a failure of explainability.
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 applications. The system is a complex neural network that learns patterns from historical hiring data. A rejected candidate asks for an explanation, but the development team cannot describe how the decision was reached. Which Microsoft responsible AI principle is most directly violated?
Why wrong: Fairness addresses bias and discrimination; while lack of transparency could hide bias, the core violation here is the inability to explain the decision.
B
Transparency
Transparency requires that systems are explainable and that users are informed about how decisions are made.
C
Privacy and security
Why wrong: Privacy and security focus on data protection, not on providing explanations for decisions.
D
Reliability and safety
Why wrong: Reliability and safety ensure the system operates correctly and without harm, but they do not directly require explainability.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Transparency
The system's inability to explain how it reached a decision violates the transparency principle, which requires AI systems to be understandable and interpretable. Complex neural networks often act as black boxes, making it impossible to provide meaningful explanations to users, directly contradicting Microsoft's responsible AI guideline that decisions should be explainable.
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 addresses bias and discrimination; while lack of transparency could hide bias, the core violation here is the inability to explain the decision.
✓
Transparency
Why this is correct
Transparency requires that systems are explainable and that users are informed about how decisions are made.
Related concept
Read the scenario before looking for a memorised answer.
✗
Privacy and security
Why it's wrong here
Privacy and security focus on data protection, not on providing explanations for decisions.
✗
Reliability and safety
Why it's wrong here
Reliability and safety ensure the system operates correctly and without harm, but they do not directly require explainability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'transparency' with 'fairness' because both relate to ethical AI, but transparency specifically requires explainability of decisions, not just absence of bias.
Detailed technical explanation
How to think about this question
Neural networks with multiple hidden layers (deep learning) learn non-linear feature interactions that are not directly interpretable by humans. Techniques like LIME or SHAP can approximate explanations, but without them, the system is a black box. In regulated industries like hiring, GDPR's 'right to explanation' mandates that automated decisions be explainable, making transparency a legal as well as ethical requirement.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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 system's inability to explain how it reached a decision violates the transparency principle, which requires AI systems to be understandable and interpretable. Complex neural networks often act as black boxes, making it impossible to provide meaningful explanations to users, directly contradicting Microsoft's responsible AI guideline that decisions should be explainable.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses an AI system to help screen job applications. The system ranks candidates based on their resumes. The company wants to ensure that if a candidate asks why they were not selected, the company can provide a clear explanation of the factors that influenced the AI's decision. Which Microsoft responsible AI principle is most directly relevant?
easy
✓ A.Transparency
B.Accountability
C.Privacy and security
D.Reliability and safety
Why A: Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, the company needs to provide a clear explanation of why a candidate was not selected, which directly aligns with transparency's goal of making AI decisions explainable to users.
Last reviewed: Jun 11, 2026
Question Discussion
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