- A
Use a fixed random seed when sampling data to ensure repeatability.
Why wrong: Using a random seed is a good practice but is more about consistent sampling than overall data quality.
- B
Shuffle the dataset before splitting into train and test sets.
Why wrong: Shuffling is a good practice for many ML tasks, but it is not specifically about data quality or reproducibility.
- C
Implement automated data validation checks to catch anomalies in new data.
Automated validation ensures data quality by catching issues early.
- D
Manually inspect and clean data to remove outliers.
Why wrong: Manual processes are not scalable and not reproducible.
- E
Save cleaned and transformed datasets to S3 with versioning enabled.
This ensures reproducibility and traceability of data used for training.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data team is preparing data for a machine learning pipeline. Which TWO practices are best for ensuring data quality and reproducibility? (Choose two.)
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Automated data validation checks can be implemented using tools like AWS Glue DataBrew's data quality rules or Apache Deequ's constraint suggestions, which compute metrics such as completeness, uniqueness, and distributional similarity. Versioning cleaned datasets to S3 with bucket versioning or using AWS Lake Formation's managed tables ensures that every transformation is traceable and can be rolled back, which is critical for auditability and reproducibility in regulated environments.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Data Preparation for Machine Learning — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement automated data validation checks to catch anomalies in new data. — Option C is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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