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 deploys an AI system to approve loan applications. The system was trained on historical data that contains systematic biases against certain ethnic groups. Despite awareness of this bias, the bank proceeds with deployment, expecting the system to correct itself over time. Which Microsoft responsible AI principle is most directly violated?
Fairness mandates that AI systems should not discriminate against individuals or groups; deploying a biased model directly violates this principle.
B
Reliability and safety
Why wrong: Reliability and safety focus on the system performing as expected and avoiding harm, but the primary violation here is discrimination, not performance failure.
C
Transparency
Why wrong: Transparency requires that users understand how decisions are made, but the core issue is the bias itself, not the lack of explainability.
D
Privacy and security
Why wrong: Privacy and security deal with protecting personal data and system integrity, not with bias in decision-making.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness
The bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.
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 this is correct
Fairness mandates that AI systems should not discriminate against individuals or groups; deploying a biased model directly violates this principle.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reliability and safety
Why it's wrong here
Reliability and safety focus on the system performing as expected and avoiding harm, but the primary violation here is discrimination, not performance failure.
✗
Transparency
Why it's wrong here
Transparency requires that users understand how decisions are made, but the core issue is the bias itself, not the lack of explainability.
✗
Privacy and security
Why it's wrong here
Privacy and security deal with protecting personal data and system integrity, not with bias in decision-making.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between Fairness and Transparency—candidates may confuse 'knowing about bias' (transparency) with 'acting on bias' (fairness), but the core violation here is the failure to ensure equitable treatment, not the lack of disclosure.
Detailed technical explanation
How to think about this question
Under the hood, fairness in ML is often measured using metrics like demographic parity (equal acceptance rates across groups) or equalized odds (equal false positive/negative rates). The bank's expectation of self-correction is technically flawed because models trained on biased data will amplify those biases over time through feedback loops—e.g., rejecting more loans from a group leads to less data from that group, further skewing the model. Real-world examples include biased credit scoring models that disproportionately deny loans to minorities, which Microsoft's Fairlearn toolkit helps detect and mitigate.
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: Fairness — The bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.
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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