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 company deploys an AI system to screen job applications and recommend candidates for interviews. The system consistently rates male candidates higher than equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?
Fairness is violated because the AI system is discriminating based on gender by systematically favoring male candidates over equally qualified female candidates.
B
Reliability and safety
Why wrong: Reliability and safety focus on the system operating consistently and safely; while important, this scenario is about bias, not system failure or safety issues.
C
Privacy and security
Why wrong: Privacy and security are about protecting data and preventing unauthorized access; the issue described is discrimination, not data leakage.
D
Inclusiveness
Why wrong: Inclusiveness ensures the system benefits everyone and does not exclude users; while related, the direct violation here is unfair treatment based on gender, which falls under fairness.
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 consistent rating of male candidates higher than equally qualified female candidates demonstrates a clear bias in outcomes based on gender, which directly violates the Fairness principle. Fairness in responsible AI requires that AI systems treat all people equitably, avoiding discrimination based on sensitive attributes such as gender, race, or age. This bias likely stems from biased training data or flawed feature engineering that encodes historical hiring disparities.
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 is violated because the AI system is discriminating based on gender by systematically favoring male candidates over equally qualified female candidates.
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 operating consistently and safely; while important, this scenario is about bias, not system failure or safety issues.
✗
Privacy and security
Why it's wrong here
Privacy and security are about protecting data and preventing unauthorized access; the issue described is discrimination, not data leakage.
✗
Inclusiveness
Why it's wrong here
Inclusiveness ensures the system benefits everyone and does not exclude users; while related, the direct violation here is unfair treatment based on gender, which falls under fairness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'Inclusiveness' (which focuses on designing for all users, including those with disabilities) with 'Fairness' (which specifically addresses bias and equitable outcomes), leading them to select D instead of A.
Trap categories for this question
Scenario analysis trap
Reliability and safety focus on the system operating consistently and safely; while important, this scenario is about bias, not system failure or safety issues.
Detailed technical explanation
How to think about this question
Under the hood, fairness violations often arise from imbalanced training datasets where one demographic group is overrepresented, or from proxy features (e.g., zip code, name) that correlate with protected attributes. In Azure Machine Learning, tools like Fairlearn can detect such disparities by computing metrics like demographic parity or equalized odds. A real-world example is an AI recruiting tool that penalized resumes containing the word 'women's' (e.g., 'women's chess club'), directly causing gender-based unfairness.
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 consistent rating of male candidates higher than equally qualified female candidates demonstrates a clear bias in outcomes based on gender, which directly violates the Fairness principle. Fairness in responsible AI requires that AI systems treat all people equitably, avoiding discrimination based on sensitive attributes such as gender, race, or age. This bias likely stems from biased training data or flawed feature engineering that encodes historical hiring disparities.
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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