MLA-C01 ML Model Development Practice Question

A company needs to detect bias in a pre-trained model before deployment. They want to compute metrics like disparate impact and equal opportunity difference. Which AWS service should they use?

⚠ Common exam trap

MLA-C01 often tests the confusion between Clarify (bias/explainability) and Model Monitor (drift/quality), so candidates who see 'model' and pick Model Monitor miss the bias-specific requirement.

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

✓

SageMaker Clarify

SageMaker Clarify is purpose-built to detect bias in datasets and models, computing pre-training and post-training bias metrics such as disparate impact (DI) and equal opportunity difference (EOD). It integrates with SageMaker training and hosting, and can also generate explainability reports using SHAP values. Because the question asks specifically for bias metrics like DI and EOD, Clarify is the correct service.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
SageMaker Clarify
Why this is correct
SageMaker Clarify computes pre-training and post-training bias metrics, including disparate impact and equal opportunity difference, directly against a pre-trained model. It satisfies the stem's requirement to detect bias before deployment by running bias analysis on model predictions without retraining, unlike SageMaker Model Monitor, which only tracks drift on live endpoints.
✗
Amazon Rekognition
Why it's wrong here
Amazon Rekognition performs image and video analysis such as facial detection and moderation, not bias metric computation. It is tempting because it processes demographic attributes, but disparate impact and equal opportunity difference require SageMaker Clarify, which calculates those statistical measures against training or evaluation data.
✗
SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor detects data drift and quality degradation in deployed endpoints, not pre-deployment bias metrics. It is tempting because it monitors model behaviour statistically, but disparate impact and equal opportunity difference are computed by SageMaker Clarify before deployment, not by runtime monitoring.
✗
SageMaker Debugger
Why it's wrong here
SageMaker Debugger captures tensors and training metrics during jobs, not fairness statistics. Computing disparate impact and equal opportunity difference requires SageMaker Clarify, which runs bias analysis on data and models. Debugger is tempting because it inspects model internals, but it detects training anomalies, not bias metrics.

Go deeper

1 more way this is tested on MLA-C01

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company uses SageMaker Clarify to detect bias during training. They want to ensure that the trained model does not rely on a sensitive attribute like gender. Which Clarify feature should they configure?

medium

✓ A.Clarify bias config with post-training bias metrics
B.Clarify with SageMaker Model Monitor
C.SHAP analysis
D.Clarify processing job with pre-training bias metrics
E.Bias report generation

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 MLA-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 MLA-C01 exam.

MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

Related to this question

1 more way this is tested on MLA-C01