MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is using SageMaker Pipelines to automate data preparation. Which TWO statements about data validation within a pipeline are correct?
⚠ Common exam trap
Watch out — candidates often assume data validation requires a trained model or is limited to training data, but SageMaker Pipelines supports rule-based validation on any dataset, including inference data, without needing a model.
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
✓
The pipeline can be configured to fail if data quality checks do not meet thresholds
SageMaker Pipelines allows you to define conditions that evaluate the output of data quality checks (e.g., using Amazon SageMaker Model Monitor or custom validation scripts). If the checks fail to meet specified thresholds (e.g., missing values exceed 5%), the pipeline can be configured to fail, stopping execution and preventing downstream steps from processing invalid data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The pipeline can be configured to fail if data quality checks do not meet thresholds
Why this is correct
You can set conditions to fail the pipeline.
- ✓
SageMaker Pipelines has a built-in 'CheckDataQuality' step for data validation
Why this is correct
CheckDataQuality is a step type for validating data quality.
- ✗
Data validation can only be performed on training data, not inference data
Why it's wrong here
Validation can be applied to any dataset.
- ✗
Data validation steps cannot pass results to subsequent steps
Why it's wrong here
Property values can be passed downstream.
- ✗
Data validation requires a trained model to evaluate predictions
Why it's wrong here
Validation is on raw data, not predictions.
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