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MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Data…
A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset for classification. They want to detect potential bias in the data before training. Which SageMaker service should they use in conjunction with Data Wrangler to detect bias?
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 designed to detect bias in datasets and models. It integrates with Data Wrangler to provide bias analysis during the data preparation phase.
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 computes bias metrics such as class imbalance and disparate impact on datasets and trained models. Running it alongside Data Wrangler lets the team quantify pre-training bias in the prepared features, which is exactly the detection the stem requires.
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Amazon SageMaker Debugger
Why it's wrong here
Debugger monitors training jobs for anomalies such as vanishing gradients and overfitting; it does not compute bias metrics on datasets. It suits debugging model training runs, whereas SageMaker Clarify provides pre-training bias detection and explainability, which is the capability required here.
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Amazon SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines orchestrates workflow steps such as processing, training and deployment; it contains no bias-detection algorithm itself. It is tempting because Pipelines can chain Data Wrangler flows into automated MLOps workflows, which suits repeatable pipeline execution. Detecting bias requires SageMaker Clarify, whose bias metrics run against the dataset.
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Amazon SageMaker Model Monitor
Why it's wrong here
SageMaker Model Monitor tracks deployed endpoints for data drift and quality violations, so it cannot inspect a pre-training dataset for bias. It is tempting because it also surfaces distribution anomalies, but that applies to live inference traffic; SageMaker Clarify is the component that computes bias metrics during data preparation.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.