- A
Inclusiveness
Why wrong: Inclusiveness aims to empower everyone and ensure AI benefits all people, but the core problem is unequal performance, which is a fairness concern. Inclusiveness is broader and does not specifically focus on eliminating outcome disparities.
- B
Fairness
Fairness directly addresses biases that cause an AI system to perform poorly for certain demographic groups. Prioritizing fairness involves seeking more representative data or adjusting the model to reduce disparities.
- C
Transparency
Why wrong: Transparency is about being open about how the AI system works and its limitations. While important, it does not directly fix the accuracy disparity; it would only explain why it happens.
- D
Accountability
Why wrong: Accountability ensures that people are responsible for AI systems and their outcomes. It does not directly address the performance gap; it focuses on governance and oversight.
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 hospital deploys an AI system to recommend treatment plans for patients. After deployment, the system is found to have significantly lower accuracy for patients from certain racial and ethnic groups because historical medical data for those groups is sparse. Which Microsoft responsible AI principle should the hospital prioritize to address this issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Fairness
The correct answer is B. Fairness. The AI system's lower accuracy for certain racial and ethnic groups is a direct fairness issue, as it produces biased outcomes due to sparse historical data. Microsoft's Fairness principle requires AI systems to treat all groups equitably and mitigate disparities in performance, which is the core problem here.
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 everyone and ensure AI benefits all people, but the core problem is unequal performance, which is a fairness concern. Inclusiveness is broader and does not specifically focus on eliminating outcome disparities.
- ✓
Fairness
Why this is correct
Fairness directly addresses biases that cause an AI system to perform poorly for certain demographic groups. Prioritizing fairness involves seeking more representative data or adjusting the model to reduce disparities.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Transparency
Why it's wrong here
Transparency is about being open about how the AI system works and its limitations. While important, it does not directly fix the accuracy disparity; it would only explain why it happens.
- ✗
Accountability
Why it's wrong here
Accountability ensures that people are responsible for AI systems and their outcomes. It does not directly address the performance gap; it focuses on governance and oversight.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse fairness with inclusiveness, thinking that including more diverse data is the same as ensuring equitable outcomes, but fairness specifically addresses the algorithmic bias that arises from data imbalances.
Detailed technical explanation
How to think about this question
Under the hood, fairness in AI often requires techniques like reweighing training samples, using adversarial debiasing, or applying fairness constraints during model training (e.g., demographic parity or equalized odds). In healthcare, a model trained on sparse data for a subgroup may have high variance and poor generalization; fairness interventions can adjust loss functions to penalize disparate error rates across groups. Real-world examples include IBM's AI Fairness 360 toolkit, which provides metrics and algorithms to detect and mitigate bias.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 correct answer is B. Fairness. The AI system's lower accuracy for certain racial and ethnic groups is a direct fairness issue, as it produces biased outcomes due to sparse historical data. Microsoft's Fairness principle requires AI systems to treat all groups equitably and mitigate disparities in performance, which is the core problem here.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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