- A
Adjust the decision threshold to equalize approval rates across groups.
Why wrong: Adjusting thresholds without proper analysis may improve fairness metrics but could degrade overall model performance and may not address underlying bias.
- B
Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.
SageMaker Clarify provides bias metrics and explanations, which is the first step in understanding and mitigating bias.
- C
Document the disparity in a compliance report and continue using the model.
Why wrong: Documentation alone does not fix the non-compliance; action must be taken to mitigate bias.
- D
Replace the model with a simpler explainable model to eliminate bias.
Why wrong: A simpler model may still exhibit bias and the institution wants to continue using the current model if possible.
Quick Answer
The answer is to run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report. This is the correct first step because SageMaker Clarify provides built-in bias detection metrics, such as Difference in Positive Proportions and Disparate Impact, which quantify the nature and extent of bias before any remediation begins. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your understanding that bias detection must precede any corrective action—a common trap is jumping to retraining or threshold adjustments without first diagnosing the source. SageMaker Clarify’s report serves as both a compliance document and a diagnostic tool to determine whether bias stems from the data, model, or decision threshold. Memory tip: think “Detect before Correct”—Clarify first, then fix.
AIF-C01 Practice Question: Security, Compliance and Governance for AI Solutions
This AIF-C01 practice question tests your understanding of security, compliance and governance for ai solutions. 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 financial institution uses Amazon SageMaker to host a model for credit scoring. The model was trained on data that includes demographic attributes. During a routine audit, the compliance team finds that the model produces significantly different approval rates for applicants of different ethnicities, even when credit profiles are similar. The institution must continue using the model but needs to ensure compliance with fair lending laws. What should the company do FIRST?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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
Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.
Amazon SageMaker Clarify is the correct first step because it provides built-in bias detection and explainability for machine learning models. Before taking any corrective action, the company must first quantify and understand the nature and extent of the bias using SageMaker Clarify's bias metrics (e.g., Difference in Positive Proportions, Disparate Impact). This diagnostic report is essential for compliance documentation and for determining whether the bias is due to the model, the data, or the threshold, thereby guiding any subsequent remediation steps.
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.
- ✗
Adjust the decision threshold to equalize approval rates across groups.
Why it's wrong here
Adjusting thresholds without proper analysis may improve fairness metrics but could degrade overall model performance and may not address underlying bias.
- ✓
Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report.
Why this is correct
SageMaker Clarify provides bias metrics and explanations, which is the first step in understanding and mitigating bias.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Document the disparity in a compliance report and continue using the model.
Why it's wrong here
Documentation alone does not fix the non-compliance; action must be taken to mitigate bias.
- ✗
Replace the model with a simpler explainable model to eliminate bias.
Why it's wrong here
A simpler model may still exhibit bias and the institution wants to continue using the current model if possible.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the principle that the first step in addressing bias is always to measure and understand it using a dedicated tool like SageMaker Clarify, rather than jumping to a corrective action like threshold adjustment or model replacement.
Detailed technical explanation
How to think about this question
SageMaker Clarify computes bias metrics such as Disparate Impact (DI) and Difference in Positive Proportions (DPPL) by comparing outcomes across sensitive groups (e.g., ethnicities). Under the hood, it uses Shapley values for feature importance and can generate a bias report in JSON or HTML format, which is critical for audit trails. In a real-world scenario, the compliance team would run Clarify on the model's predictions against a test dataset that includes the sensitive attribute, and the resulting report would show whether the bias is pre-existing in the training data (pre-training bias) or introduced by the model (post-training 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Security, Compliance and Governance for AI Solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Security, Compliance and Governance for AI Solutions — This question tests Security, Compliance and Governance for AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Run Amazon SageMaker Clarify to analyze the model for bias and generate a bias report. — Amazon SageMaker Clarify is the correct first step because it provides built-in bias detection and explainability for machine learning models. Before taking any corrective action, the company must first quantify and understand the nature and extent of the bias using SageMaker Clarify's bias metrics (e.g., Difference in Positive Proportions, Disparate Impact). This diagnostic report is essential for compliance documentation and for determining whether the bias is due to the model, the data, or the threshold, thereby guiding any subsequent remediation steps.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 30, 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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