The answer is Transparency, as it is the Microsoft responsible AI principle that requires AI systems to provide clear, understandable explanations for their decisions. In the context of loan approvals, transparency ensures that when customers are denied, the bank can articulate why the AI reached that conclusion—covering the model’s purpose, limitations, and reasoning in accessible terms. On the AI-900 exam, this scenario tests your ability to distinguish transparency from other principles like fairness or accountability; a common trap is confusing it with fairness, which focuses on bias mitigation rather than explainability. Microsoft’s principle of transparency directly addresses the need for explainability in loan decisions, making it the correct choice when users demand justification for automated outcomes. A helpful memory tip: think of transparency as a “glass box” that lets you see inside the AI’s reasoning, unlike a “black box” that hides it.
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 bank uses an AI system to approve personal loans. Some customers whose loans were denied have asked for an explanation of why their application was rejected. Which Microsoft responsible AI principle requires the bank to provide these explanations?
Why wrong: Fairness is about ensuring AI does not discriminate against groups, but it does not directly require providing individual explanations for decisions.
B
Reliability and safety
Why wrong: Reliability and safety ensure the system performs consistently and safely, but they do not mandate explanations for specific outcomes.
C
Transparency
Transparency means that AI systems should be understandable and that individuals should be able to get explanations for decisions that affect them. This principle directly addresses the need for explanations.
D
Accountability
Why wrong: Accountability means that organizations should take responsibility for their AI systems, but it does not specifically require providing explanations to individuals.
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 correct principle because it requires AI systems to be understandable and for decisions to be explainable to users. In this scenario, customers denied loans have a right to know why the AI made that decision, which aligns with Microsoft's principle of transparency—ensuring that AI systems communicate their purpose, limitations, and reasoning in clear, accessible terms.
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 about ensuring AI does not discriminate against groups, but it does not directly require providing individual explanations for decisions.
✗
Reliability and safety
Why it's wrong here
Reliability and safety ensure the system performs consistently and safely, but they do not mandate explanations for specific outcomes.
✓
Transparency
Why this is correct
Transparency means that AI systems should be understandable and that individuals should be able to get explanations for decisions that affect them. This principle directly addresses the need for explanations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accountability
Why it's wrong here
Accountability means that organizations should take responsibility for their AI systems, but it does not specifically require providing explanations to individuals.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse accountability (who is responsible) with transparency (what is explained), but the question specifically asks for the principle that requires providing explanations to customers, which is transparency.
Detailed technical explanation
How to think about this question
Under the hood, 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 that explain individual predictions. In a loan approval model, this could mean showing that a denial was primarily due to a low credit score or high debt-to-income ratio, rather than a black-box output. Real-world scenarios like GDPR's 'right to explanation' in automated decision-making make transparency a legal requirement, not just an ethical guideline.
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 correct principle because it requires AI systems to be understandable and for decisions to be explainable to users. In this scenario, customers denied loans have a right to know why the AI made that decision, which aligns with Microsoft's principle of transparency—ensuring that AI systems communicate their purpose, limitations, and reasoning in clear, accessible terms.
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 bank deploys an AI system to approve personal loans. The system uses a complex deep learning model that produces a decision (approve or reject) without any explanation of why. Loan applicants who are rejected are not given any reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this system?
hard
A.Fairness
✓ B.Transparency
C.Reliability and safety
D.Privacy and security
Why B: The system's inability to provide any explanation for its loan approval or rejection decisions directly violates the transparency principle. Microsoft's responsible AI principle of transparency requires that AI systems be understandable and that users be informed about how decisions are made, including the factors that influenced the outcome. A black-box deep learning model that gives no reasoning or feedback to rejected applicants fails this requirement.
Variation 2. A hospital deploys an AI system to assist in diagnosing diseases from medical images. The system is a complex deep learning model that provides a diagnosis without any explanation. Doctors are skeptical and want to understand why the system made a particular recommendation. The hospital decides to deploy the system without providing any interpretability. Which Microsoft responsible AI principle is most directly being violated?
hard
A.Fairness
B.Reliability & Safety
✓ C.Transparency
D.Inclusiveness
Why C: The system provides a diagnosis without any explanation of how it reached its conclusion, and the hospital decides to deploy it without interpretability. This directly violates the transparency principle, which requires AI systems to be understandable and for their decisions to be explainable to users, especially in high-stakes domains like healthcare.
Variation 3. A hospital deploys an AI system to assist doctors in interpreting MRI scans. The system highlights the regions of interest and provides a numeric confidence score for its findings, along with a list of the image features that contributed to the diagnosis. Which responsible AI principle is being applied?
medium
A.Fairness
✓ B.Transparency
C.Privacy
D.Accountability
Why B: The system provides a numeric confidence score and a list of image features that contributed to the diagnosis, which directly supports the principle of Transparency. Transparency in responsible AI requires that AI systems are understandable and that their decisions can be explained to users, enabling clinicians to interpret and trust the output.
Last reviewed: Jun 11, 2026
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