- A
Establish a governance process for regular auditing and human review of decisions
Governance and human oversight are critical for responsible AI, as emphasized in the NIST framework.
- B
Measure bias metrics across demographic groups using SageMaker Clarify
Measuring bias is a core step in the NIST framework to identify risks.
- C
Optimize the model solely for overall accuracy
Why wrong: Optimizing only for accuracy can ignore fairness; metrics should include fairness and performance.
- D
Ensure the training data includes diverse representation across demographic groups
Diverse data reduces representation bias and is a key mitigation strategy.
- E
Remove all sensitive attributes (e.g., gender, race) from the dataset
Why wrong: Removing sensitive attributes does not eliminate proxy discrimination; other features may correlate with protected attributes.
AIF-C01 Developing an AI system for resume screening Practice Question
This AIF-C01 practice question tests your understanding of developing an ai system for resume screening. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 is developing an AI system for resume screening. They want to ensure fairness and reduce bias. Which THREE steps should they take in accordance with the NIST AI Risk Management Framework and AWS responsible AI principles?
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
Establish a governance process for regular auditing and human review of decisions
The NIST AI Risk Management Framework emphasizes measuring bias, ensuring diverse data, and establishing governance. Removing sensitive features alone is insufficient due to proxy correlations. Relying solely on accuracy ignores fairness. The three correct steps cover measurement, data diversity, and governance.
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.
- ✓
Establish a governance process for regular auditing and human review of decisions
Why this is correct
Governance and human oversight are critical for responsible AI, as emphasized in the NIST framework.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Measure bias metrics across demographic groups using SageMaker Clarify
Why this is correct
Measuring bias is a core step in the NIST framework to identify risks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Optimize the model solely for overall accuracy
Why it's wrong here
Optimizing only for accuracy can ignore fairness; metrics should include fairness and performance.
- ✓
Ensure the training data includes diverse representation across demographic groups
Why this is correct
Diverse data reduces representation bias and is a key mitigation strategy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all sensitive attributes (e.g., gender, race) from the dataset
Why it's wrong here
Removing sensitive attributes does not eliminate proxy discrimination; other features may correlate with protected attributes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
Got this wrong? Here's your next step.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Establish a governance process for regular auditing and human review of decisions — The NIST AI Risk Management Framework emphasizes measuring bias, ensuring diverse data, and establishing governance. Removing sensitive features alone is insufficient due to proxy correlations. Relying solely on accuracy ignores fairness. The three correct steps cover measurement, data diversity, and governance.
What should I do if I get this AIF-C01 question wrong?
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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