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 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?
Why wrong: Fairness is about ensuring AI systems do not discriminate against groups. While the system could be unfair, the primary issue described is the lack of explanation, not bias.
B
Transparency
Correct. Transparency requires that AI systems be understandable and their decisions explainable. The bank's system provides no explanation for loan decisions, directly violating this principle.
C
Reliability and safety
Why wrong: Reliability and safety concern the system's accuracy and ability to operate without causing harm. The scenario does not indicate inaccuracies or safety issues.
D
Privacy and security
Why wrong: Privacy and security involve protecting personal data. The scenario does not mention any data breach or misuse of data.
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 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.
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 systems do not discriminate against groups. While the system could be unfair, the primary issue described is the lack of explanation, not bias.
✓
Transparency
Why this is correct
Correct. Transparency requires that AI systems be understandable and their decisions explainable. The bank's system provides no explanation for loan decisions, directly violating this principle.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reliability and safety
Why it's wrong here
Reliability and safety concern the system's accuracy and ability to operate without causing harm. The scenario does not indicate inaccuracies or safety issues.
✗
Privacy and security
Why it's wrong here
Privacy and security involve protecting personal data. The scenario does not mention any data breach or misuse of data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the lack of explanation with fairness or privacy issues, but the core violation is the absence of transparency, which is explicitly about providing understandable reasoning for AI decisions.
Trap categories for this question
Scenario analysis trap
Reliability and safety concern the system's accuracy and ability to operate without causing harm. The scenario does not indicate inaccuracies or safety issues.
Detailed technical explanation
How to think about this question
Transparency in AI often involves techniques like explainable AI (XAI), such as LIME or SHAP, which generate feature importance scores to show why a model made a particular prediction. In deep learning, this is challenging due to the non-linear transformations in hidden layers, but methods like integrated gradients or attention mechanisms can provide partial interpretability. Without such mechanisms, the system operates as a complete black box, violating the principle that users should be able to understand and contest decisions.
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 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.
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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