easyMultiple Choice
AIF-C01 Practice Question: A financial services company is deploying a…
A financial services company is deploying a machine learning model to approve loans. They want to ensure that the model does not discriminate based on race or gender. Which AWS service or feature can help them detect bias in the model's predictions?
⚠ Common exam trap
AIF-C01 often tests the distinction between SageMaker Clarify (bias/fairness detection) and SageMaker Model Monitor (drift/quality monitoring) — candidates confuse the two because both are 'SageMaker monitoring' features.
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
✓
Amazon SageMaker Clarify
Amazon SageMaker Clarify is purpose-built to detect bias in ML models and datasets. It computes bias metrics such as Disparate Impact (DI) and Difference in Conditional Acceptance (DCA) across protected attributes like race and gender, and also provides explainability via SHAP values. This directly addresses the requirement to detect discrimination in loan approval predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker Clarify
Why this is correct
SageMaker Clarify runs bias metrics such as disparate impact across protected attributes like race and gender, both pre-training and on model predictions. This directly satisfies the requirement to detect discrimination in loan approval outputs, unlike general monitoring or debugging tools.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail records API activity and account events, capturing no model predictions or outcome labels, so bias cannot be measured. It is tempting because it provides an audit trail of who did what, and would be correct for investigating unauthorised access or configuration changes.
- ✗
Amazon SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data drift, model quality degradation and bias drift in deployed endpoints, but it does not measure bias in predictions before deployment. It is tempting because it is the SageMaker monitoring feature, and would be correct for ongoing production drift detection after the model is live.
- ✗
AWS Identity and Access Management (IAM)
Why it's wrong here
IAM controls authentication and authorisation for AWS resources, holding no prediction data and computing no fairness metrics. It is tempting because access control feels related to governance, and would be correct for restricting which principals may invoke the model or read training data.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.