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MLA-C01 Practice Question: A data team is using Amazon SageMaker Data…
A data team is using Amazon SageMaker Data Wrangler to prepare a dataset. They need to detect potential bias in the data before training a model. Which feature of Data Wrangler should they use?
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
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Bias detection with Amazon SageMaker Clarify
Data Wrangler integrates with Amazon SageMaker Clarify for bias detection. Therefore, the correct answer is D: Bias detection with Amazon SageMaker Clarify. Options A and B are features of Data Wrangler but not specifically for bias detection. Option C (Export to Feature Store) is unrelated to bias detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Visual data profiling
Why it's wrong here
Visual data profiling summarises distributions, missing values and outliers; it does not compute bias metrics such as class imbalance or disparate impact across groups. It is tempting because profiling is the natural first pass over a dataset, and it would be the right choice when the goal is simply to understand data quality before cleaning.
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Built-in transforms for missing values
Why it's wrong here
Imputing missing values changes incomplete records but produces no bias measurement; it cannot quantify imbalance or disparate impact across sensitive groups. It is tempting because missing-value handling is a core Data Wrangler transform, and it would be correct when the requirement is to repair nulls before training rather than to detect bias.
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Export to Feature Store
Why it's wrong here
Exporting to Feature Store publishes engineered features for reuse in training and inference; it performs no bias analysis and returns no metric. It is tempting because it is the standard final step of a Data Wrangler flow, and it would be correct when the goal is to make curated features available to multiple models.
- ✓
Bias detection with Amazon SageMaker Clarify
Why this is correct
SageMaker Clarify bias detection integrates into Data Wrangler, computing metrics such as class imbalance and disparate impact across facets before training. This satisfies the requirement to detect potential bias in the prepared dataset prior to model training.
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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.