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 city council deploys an AI system to analyze surveillance footage and automatically issue traffic violation fines. They want to ensure the system does not disproportionately target one type of vehicle (e.g., bicycles over cars) when issuing fines. Which Microsoft responsible AI principle is most directly relevant?
Why wrong: Inclusiveness aims to empower all people, but the specific concern here is equitable treatment across vehicle types, which falls under Fairness.
B
Fairness
Fairness ensures that AI decisions do not create biased outcomes across different categories, such as vehicle types.
C
Reliability and safety
Why wrong: Reliability and safety are about system performance and avoiding harm, not specifically about equitable treatment.
D
Transparency
Why wrong: Transparency is about being open about how the system works, not ensuring equal treatment across groups.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Fairness
The scenario describes a risk of algorithmic bias where the AI system might disproportionately issue fines to bicycles over cars. The Microsoft responsible AI principle of Fairness directly addresses this by requiring that AI systems treat all groups equitably and avoid discrimination based on protected attributes. Ensuring fairness involves auditing the model's predictions across different vehicle types and mitigating any statistical 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.
✗
Inclusiveness
Why it's wrong here
Inclusiveness aims to empower all people, but the specific concern here is equitable treatment across vehicle types, which falls under Fairness.
✓
Fairness
Why this is correct
Fairness ensures that AI decisions do not create biased outcomes across different categories, such as vehicle types.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reliability and safety
Why it's wrong here
Reliability and safety are about system performance and avoiding harm, not specifically about equitable treatment.
✗
Transparency
Why it's wrong here
Transparency is about being open about how the system works, not ensuring equal treatment across groups.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between Fairness and Inclusiveness, where candidates mistakenly choose Inclusiveness because it sounds related to avoiding bias, but Inclusiveness is about designing for diverse user needs, not preventing discriminatory outcomes in automated decisions.
Detailed technical explanation
How to think about this question
Under the hood, fairness in this context is often measured using metrics like demographic parity (equal positive rate across groups) or equalized odds (equal true positive and false positive rates). In practice, a fairness audit would involve computing the fine issuance rate per vehicle type and testing for statistically significant differences, then applying techniques such as reweighting training data or post-processing model outputs to reduce bias. Real-world examples include Amazon's scrapped hiring tool that penalized resumes containing the word 'women's' and COMPAS recidivism scores showing racial disparities.
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 scenario describes a risk of algorithmic bias where the AI system might disproportionately issue fines to bicycles over cars. The Microsoft responsible AI principle of Fairness directly addresses this by requiring that AI systems treat all groups equitably and avoid discrimination based on protected attributes. Ensuring fairness involves auditing the model's predictions across different vehicle types and mitigating any statistical 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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