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MLA-C01 Practice Question: A team is using Amazon SageMaker Data Wrangler to…
A team is using Amazon SageMaker Data Wrangler to prepare a large dataset. They need to detect potential bias in the data before training. Which capability of Data Wrangler should they use?
⚠ Common exam trap
A common mix-up: candidates confuse data preprocessing techniques (like SMOTE for oversampling) with bias detection, or assume that any AWS query service (like Athena) can perform bias analysis, when only SageMaker Clarify provides the dedicated bias detection and reporting capability integrated with Data Wrangler.
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
✓
Integration with Amazon SageMaker Clarify for bias reports
Amazon SageMaker Data Wrangler integrates directly with Amazon SageMaker Clarify to detect bias in datasets. This integration allows you to run bias analysis on your data before training, generating reports that highlight potential imbalances or unfairness in features and target variables. It is the correct capability for the team's stated need.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Integration with Amazon SageMaker Clarify for bias reports
Why this is correct
SageMaker Clarify runs bias detection against the prepared dataset, computing metrics such as class imbalance and disparate impact across facets. Data Wrangler surfaces these Clarify bias reports directly in its analysis view, letting the team identify imbalance before training rather than after model deployment.
- ✗
Built-in transform for SMOTE oversampling
Why it's wrong here
SMOTE oversampling synthesises minority-class examples to rebalance a dataset; it remedies imbalance but does not measure or report bias. It is tempting because oversampling is a common fairness-adjacent preprocessing step. The scenario asks to detect potential bias, which requires Data Wrangler's bias report analysing facet distributions, not a resampling transform.
- ✗
Use of Amazon Athena to query data for bias patterns
Why it's wrong here
Athena queries data held in Amazon S3 for analysis, but it computes no bias metrics and sits outside Data Wrangler's built-in profiling. It is tempting because SQL exploration feels like a way to inspect class distributions. Detecting bias requires Data Wrangler's bias report, which evaluates facet imbalance and labels directly within the preparation flow.
- ✗
Export to Amazon SageMaker Feature Store
Why it's wrong here
Feature Store stores and serves engineered features for training and inference; it performs no bias detection. It is tempting because it is a core Data Wrangler export destination in ML pipelines. Detecting bias requires the built-in bias report, which analyses distributions across facets before training rather than persisting transformed data.
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