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 is developing an AI system to automatically approve personal loans. To ensure the system does not discriminate against any group of applicants, which Microsoft responsible AI principle should the bank primarily focus on?
Why wrong: Accountability requires that the people who design and deploy AI systems are responsible for their operation, but it does not directly focus on preventing discrimination.
B
Inclusiveness
Why wrong: Inclusiveness aims to empower everyone by designing for a wide range of users, but it does not specifically target the elimination of bias in decision-making.
C
Fairness
Fairness is the principle that AI systems should treat all people equitably and avoid bias, making it the correct focus for preventing discrimination in loan approvals.
D
Reliability and Safety
Why wrong: Reliability and Safety ensure that AI systems operate correctly and safely under expected conditions, but they do not directly address unfair treatment of groups.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness
Fairness is the correct principle because it directly addresses the need to prevent discrimination in AI systems, such as loan approval models. By focusing on fairness, the bank ensures that the model's predictions do not systematically disadvantage any group based on protected attributes like race, gender, or age, which is critical for ethical and legal compliance.
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.
✗
Accountability
Why it's wrong here
Accountability requires that the people who design and deploy AI systems are responsible for their operation, but it does not directly focus on preventing discrimination.
✗
Inclusiveness
Why it's wrong here
Inclusiveness aims to empower everyone by designing for a wide range of users, but it does not specifically target the elimination of bias in decision-making.
✓
Fairness
Why this is correct
Fairness is the principle that AI systems should treat all people equitably and avoid bias, making it the correct focus for preventing discrimination in loan approvals.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reliability and Safety
Why it's wrong here
Reliability and Safety ensure that AI systems operate correctly and safely under expected conditions, but they do not directly address unfair treatment of groups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Inclusiveness (which is about user empowerment and accessibility) with Fairness (which is specifically about preventing discrimination and bias in model outcomes), leading them to select B instead of C.
Detailed technical explanation
How to think about this question
Under the hood, fairness in AI involves techniques like disparate impact analysis, equalized odds, and demographic parity to measure and mitigate bias in training data and model predictions. For example, a loan approval model might use a fairness metric such as the '80% rule' (four-fifths rule) from US employment law to check if the approval rate for a protected group is less than 80% of the most favored group. Real-world scenarios, like the Apple Card gender bias controversy, highlight how even unintentional correlations in features (e.g., zip code or spending habits) can lead to unfair outcomes, requiring careful preprocessing and post-processing adjustments.
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 — Fairness is the correct principle because it directly addresses the need to prevent discrimination in AI systems, such as loan approval models. By focusing on fairness, the bank ensures that the model's predictions do not systematically disadvantage any group based on protected attributes like race, gender, or age, which is critical for ethical and legal compliance.
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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