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 personal loan applications. After six months, an audit reveals that applicants from certain postal codes receive significantly lower approval rates than applicants from other postal codes, even when their income and credit scores are comparable. Which Microsoft responsible AI principle is most directly violated by this outcome?
Fairness requires AI systems to avoid discrimination; unequal approval rates based on postal code indicate a fairness violation.
B
Transparency
Why wrong: Transparency is about explaining decisions, but the core problem here is the discriminatory outcome, not the lack of explanation.
C
Inclusiveness
Why wrong: Inclusiveness aims to empower everyone, including people with disabilities, but the issue is unequal treatment based on geography, not accessibility.
D
Reliability and safety
Why wrong: Reliability and safety ensure the system functions as intended, but the system may be functioning 'correctly' based on biased data; the violation is in the outcome.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness
The AI system's approval decisions produce systematically different outcomes for applicants from different postal codes despite comparable income and credit scores, which directly violates the Fairness principle. Fairness requires that AI systems treat all individuals and groups equitably, avoiding discrimination based on sensitive attributes like location. The audit evidence shows the model has learned spurious correlations between postal code and loan risk, leading to biased approval rates.
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 requires AI systems to avoid discrimination; unequal approval rates based on postal code indicate a fairness violation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Transparency
Why it's wrong here
Transparency is about explaining decisions, but the core problem here is the discriminatory outcome, not the lack of explanation.
✗
Inclusiveness
Why it's wrong here
Inclusiveness aims to empower everyone, including people with disabilities, but the issue is unequal treatment based on geography, not accessibility.
✗
Reliability and safety
Why it's wrong here
Reliability and safety ensure the system functions as intended, but the system may be functioning 'correctly' based on biased data; the violation is in the outcome.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between Fairness (outcome-based equity) and Transparency (explainability), so candidates mistakenly choose Transparency when they see an audit revealing bias, thinking the issue is that the model's reasoning isn't clear.
Detailed technical explanation
How to think about this question
Under the hood, this bias likely originates from the training data where historical loan approvals were correlated with postal codes due to redlining or socioeconomic patterns, causing the model to learn postal code as a proxy for default risk. In production, the model's decision boundary may assign higher weight to postal code features even when income and credit scores are controlled, a classic case of disparate impact. Real-world examples include mortgage lending models that inadvertently penalized minority neighborhoods despite equivalent financial profiles, leading to regulatory actions under fair lending laws.
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 AI system's approval decisions produce systematically different outcomes for applicants from different postal codes despite comparable income and credit scores, which directly violates the Fairness principle. Fairness requires that AI systems treat all individuals and groups equitably, avoiding discrimination based on sensitive attributes like location. The audit evidence shows the model has learned spurious correlations between postal code and loan risk, leading to biased approval rates.
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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