MLA-C01 Data Preparation for Machine Learning Practice Question
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.)
⚠ Common exam trap
A common mix-up: candidates 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.
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
✓
Use Data Wrangler to redact or remove PII columns from the dataset before training.
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.
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.
- ✗
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.
Why this is correct
Encryption protects data at rest.
- ✓
Use Data Wrangler transforms to anonymize or hash PII columns.
Why this is correct
Anonymization helps protect privacy while retaining data utility.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.