- A
Use Amazon Comprehend to detect PII and then a custom script to mask
Why wrong: Amazon Comprehend is designed for natural language processing and PII detection, but it is overkill and cost-prohibitive for simple regex-based masking. It does not scale as well as a distributed PySpark job for large CSV files.
- B
Use a custom script in AWS Glue Python shell job with regex
Why wrong: AWS Glue Python shell jobs run on a single node and cannot scale to process 500 GB of data efficiently. They are suitable for small datasets or lightweight transformations, but not for large-scale data masking.
- C
Use AWS Glue with a custom PySpark UDF to apply regex masking
AWS Glue with a custom PySpark UDF leverages distributed computing to process large datasets in parallel, making it the most scalable approach for masking credit card numbers across 500 GB of CSV files.
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 company needs to anonymize personally identifiable information (PII) in a dataset before using it for ML. The dataset is stored in S3 as CSV files. The team wants to mask credit card numbers by replacing all digits except the last four with asterisks. Which approach is the most scalable?
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 AWS Glue with a custom PySpark UDF to apply regex masking
Option C is the most scalable because AWS Glue with PySpark UDFs can process large datasets (e.g., 500 GB) in a distributed manner across multiple nodes, handling the masking transformation efficiently. Option A (Amazon Comprehend) adds unnecessary cost and latency for simple regex-based masking, and Option B (Python shell job) runs on a single node and does not scale to handle 500 GB effectively.
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 Amazon Comprehend to detect PII and then a custom script to mask
Why it's wrong here
Amazon Comprehend is designed for natural language processing and PII detection, but it is overkill and cost-prohibitive for simple regex-based masking. It does not scale as well as a distributed PySpark job for large CSV files.
- ✗
Use a custom script in AWS Glue Python shell job with regex
Why it's wrong here
AWS Glue Python shell jobs run on a single node and cannot scale to process 500 GB of data efficiently. They are suitable for small datasets or lightweight transformations, but not for large-scale data masking.
- ✓
Use AWS Glue with a custom PySpark UDF to apply regex masking
Why this is correct
AWS Glue with a custom PySpark UDF leverages distributed computing to process large datasets in parallel, making it the most scalable approach for masking credit card numbers across 500 GB of CSV files.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume Python shell jobs are simpler and therefore more scalable, but for large datasets (500 GB), distributed processing with PySpark is necessary. The trap is choosing a single-node solution when data volume exceeds its capacity.
Detailed technical explanation
How to think about this question
AWS Glue Python shell jobs run in a single-node environment with up to 16 GB of memory and can process files line-by-line using Python's built-in CSV module and re library, which is ideal for row-level transformations like regex masking. The regex pattern `\d(?=\d{4})` can be used to match digits before the last four, replacing them with asterisks, and this approach scales by parallelizing across multiple files or partitions using S3 event triggers or Glue workflows. In contrast, PySpark UDFs incur serialization overhead between JVM and Python processes, which degrades performance for simple string operations.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
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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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: Use AWS Glue with a custom PySpark UDF to apply regex masking — Option C is the most scalable because AWS Glue with PySpark UDFs can process large datasets (e.g., 500 GB) in a distributed manner across multiple nodes, handling the masking transformation efficiently. Option A (Amazon Comprehend) adds unnecessary cost and latency for simple regex-based masking, and Option B (Python shell job) runs on a single node and does not scale to handle 500 GB effectively.
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 24, 2026
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