MLA-C01 Data Preparation for Machine Learning Practice Question
A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)
⚠ Common exam trap
AWS often tests the distinction between practices that improve data quality (automated validation, versioning) versus practices that improve model training stability (fixed seed, shuffling), leading candidates to mistakenly select options that only address repeatability of random processes.
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
✓
Implement automated data validation checks to catch anomalies in new data.
Automated data validation checks (e.g., using AWS Glue DataBrew or Deequ on Amazon EMR) proactively catch schema drift, missing values, and distribution anomalies in new data, ensuring that only high-quality data enters the ML pipeline. This practice is essential for maintaining data quality at scale without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a fixed random seed when sampling data to ensure repeatability.
Why it's wrong here
Using a random seed is a good practice but is more about consistent sampling than overall data quality.
- ✗
Shuffle the dataset before splitting into train and test sets.
Why it's wrong here
Shuffling is a good practice for many ML tasks, but it is not specifically about data quality or reproducibility.
- ✓
Implement automated data validation checks to catch anomalies in new data.
Why this is correct
Automated validation ensures data quality by catching issues early.
- ✗
Manually inspect and clean data to remove outliers.
Why it's wrong here
Manual processes are not scalable and not reproducible.
- ✓
Save cleaned and transformed datasets to S3 with versioning enabled.
Why this is correct
This ensures reproducibility and traceability of data used for training.
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