The answer is to use Data Wrangler transforms to anonymize or hash PII columns. This is correct because Amazon SageMaker Data Wrangler includes built-in transforms specifically designed for handling PII in SageMaker Data Wrangler, allowing you to redact, mask, or hash sensitive fields like email addresses and credit card numbers directly within the data flow, ensuring compliance with data privacy regulations before the data reaches training. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of SageMaker’s native data preparation capabilities versus manual scripting—a common trap is to overcomplicate by suggesting external tools or custom code when a one-click transform exists. Remember the mnemonic “PII to PII: Protect, Identify, Isolate” to recall that Data Wrangler’s transforms let you protect sensitive data by identifying and isolating it through hashing or redaction, keeping your pipeline audit-ready.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Data Wrangler to redact or remove PII columns from the dataset before training.
Option B is correct because Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.
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.
✗
Include the raw PII in the training dataset and rely on the model to not memorize it.
Why it's wrong here
Including PII is a privacy risk and may violate regulations.
✓
Use Data Wrangler to redact or remove PII columns from the dataset before training.
Why this is correct
Removing PII columns ensures they are not used in training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue to copy the data to a separate bucket without any transformations.
Why it's wrong here
Copying without transformation does not address PII concerns.
✓
Configure Data Wrangler to output the prepared data to an S3 bucket with server-side encryption enabled.
Read the scenario before looking for a memorised answer.
✓
Use Data Wrangler transforms to anonymize or hash PII columns.
Why this is correct
Anonymization helps protect privacy while retaining data utility.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think copying data to a separate bucket (Option C) or relying on model non-memorization (Option A) is sufficient for compliance, when in fact active transformation or removal of PII is required by regulations like GDPR or CCPA.
Detailed technical explanation
How to think about this question
Data Wrangler's PII redaction transform uses pattern-based detection (e.g., regex for email addresses, Luhn algorithm for credit card numbers) to identify and mask or remove sensitive fields before the data enters the training pipeline. Server-side encryption (SSE-S3 or SSE-KMS) on the output S3 bucket ensures data at rest is encrypted, adding a layer of security for the prepared dataset. Hashing PII columns (e.g., using SHA-256) can pseudonymize data, but note that hashing alone may still be reversible if the hash space is small or if rainbow tables are used, so it should be combined with salting for stronger anonymization.
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.
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: Use Data Wrangler to redact or remove PII columns from the dataset before training. — Option B is correct because Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.
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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