- A
AI systems should run as fast as possible regardless of accuracy
Why wrong: Speed vs accuracy is a performance consideration — fairness is about equitable treatment across groups.
- B
AI systems should not perpetuate or amplify societal biases against specific groups
Fair AI doesn't discriminate based on protected characteristics and doesn't amplify historical biases present in training data.
- C
AI systems should be available 24/7 without any downtime
Why wrong: 24/7 availability is a reliability consideration — fairness is about equitable treatment across demographic groups.
- D
AI systems should always produce the same output for the same input
Why wrong: Deterministic output is a consistency property — fairness is about equal treatment across different groups of people.
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.
Which of the following is a consideration for responsible AI regarding fairness?
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
AI systems should not perpetuate or amplify societal biases against specific groups
Fairness in responsible AI means that AI systems should be designed and tested to avoid perpetuating or amplifying societal biases against specific groups. This involves careful data selection, bias detection, and mitigation techniques to ensure equitable outcomes across different demographics.
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.
- ✗
AI systems should run as fast as possible regardless of accuracy
Why it's wrong here
Speed vs accuracy is a performance consideration — fairness is about equitable treatment across groups.
- ✓
AI systems should not perpetuate or amplify societal biases against specific groups
Why this is correct
Fair AI doesn't discriminate based on protected characteristics and doesn't amplify historical biases present in training data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AI systems should be available 24/7 without any downtime
Why it's wrong here
24/7 availability is a reliability consideration — fairness is about equitable treatment across demographic groups.
- ✗
AI systems should always produce the same output for the same input
Why it's wrong here
Deterministic output is a consistency property — fairness is about equal treatment across different groups of people.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse fairness with other responsible AI principles like reliability (uptime) or consistency (determinism), leading them to pick options that sound reasonable but are not specifically about fairness.
Trap categories for this question
Command / output trap
Deterministic output is a consistency property — fairness is about equal treatment across different groups of people.
Detailed technical explanation
How to think about this question
Under the hood, fairness is often assessed using metrics like demographic parity, equal opportunity, or disparate impact, which compare prediction rates across protected groups. For example, a loan approval model might show that applicants from a certain zip code receive approvals at a significantly lower rate, indicating a potential fairness violation that requires rebalancing the training data or adjusting model thresholds.
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: AI systems should not perpetuate or amplify societal biases against specific groups — Fairness in responsible AI means that AI systems should be designed and tested to avoid perpetuating or amplifying societal biases against specific groups. This involves careful data selection, bias detection, and mitigation techniques to ensure equitable outcomes across different demographics.
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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Last reviewed: Jun 11, 2026
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