The correct choice is to replace the custom Python Map transform with AWS Glue’s built-in Mask transform, because it guarantees complete obfuscation of sensitive data like credit card numbers by applying a predefined masking pattern—such as replacing all digits with a fixed string like "XXXX-XXXX-XXXX-XXXX"—regardless of input format or partial matches. Custom regex-based transforms are error-prone and can leave fragments exposed, as seen when last four digits slip through, whereas the Mask transform uses deterministic logic built into Glue Studio and the Glue API to enforce full masking every time. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of Glue’s native data protection capabilities versus custom code, a common trap where engineers over-rely on manual transforms instead of leveraging purpose-built tools. Remember: when the policy demands absolute masking, let Glue’s Mask transform handle the heavy lifting—think “Mask it, don’t risk it.”
DEA-C01 Data Security and Governance Practice Question
This DEA-C01 practice question tests your understanding of data security and governance. 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 financial services company uses AWS Glue ETL jobs to process credit card transaction data stored in Amazon S3. The data includes PII such as names and credit card numbers. The security team requires that all PII be masked before the data is written to the curated zone of the data lake. The data engineer has implemented a Glue job that reads from the raw zone, applies a custom transform to mask credit card numbers using a regular expression, and writes to the curated zone. However, during a recent audit, the security team discovered that some masked data still contained partial credit card numbers (e.g., showing the last four digits) when viewed by analysts who should only see masked data. The company's policy is that credit card numbers must be completely masked, showing only asterisks or a fixed string like "XXXX-XXXX-XXXX-XXXX". The Glue job uses a DynamicFrame and applies a Map transform with a Python function that replaces digits with 'X'. The data is stored in Parquet format. What should the data engineer do to ensure complete masking of credit card numbers?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replace the custom Python Map transform with a built-in Glue Transform for data masking, such as the Mask transform available in Glue Studio.
Option C is correct because AWS Glue provides a built-in Mask transform that can be applied directly in Glue Studio or via the AWS Glue API. This transform is designed to reliably obfuscate sensitive data like credit card numbers by replacing them with a fixed string (e.g., 'XXXX-XXXX-XXXX-XXXX') or asterisks, ensuring complete masking regardless of input format. The custom Python Map transform in the current implementation is error-prone because it relies on a regular expression that may not catch all patterns or partial digits, whereas the Mask transform uses predefined logic to guarantee full masking.
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 an AWS Glue crawler to classify the data and apply a masking rule based on the classification.
Why it's wrong here
Crawlers classify schemas but do not mask data; masking must be part of the ETL job.
✗
Enable server-side encryption with AWS KMS on the curated S3 bucket.
Why it's wrong here
Encryption protects data at rest but does not mask the content for authorized users.
✓
Replace the custom Python Map transform with a built-in Glue Transform for data masking, such as the Mask transform available in Glue Studio.
Why this is correct
Built-in masking transforms are designed to handle common patterns and ensure complete masking.
Related concept
Read the scenario before looking for a memorised answer.
✗
Change the output format from Parquet to CSV and use a different write mode.
Why it's wrong here
Format change does not fix masking logic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume any custom Python logic with a regex is sufficient for masking, but the exam tests the understanding that AWS Glue's built-in Mask transform provides a more reliable and policy-compliant solution for sensitive data obfuscation.
Detailed technical explanation
How to think about this question
The AWS Glue Mask transform internally uses a set of predefined patterns (e.g., for credit card numbers, SSNs, email addresses) and replaces matched values with a configurable replacement string, such as 'XXXX-XXXX-XXXX-XXXX'. This transform operates on DynamicFrames and can be chained with other transforms in a Glue ETL script, ensuring consistent masking across all rows. In contrast, a custom Python function using a regular expression may fail to handle edge cases like credit card numbers with spaces, dashes, or varying lengths, leading to partial exposure.
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 Security and Governance — This question tests Data Security and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Replace the custom Python Map transform with a built-in Glue Transform for data masking, such as the Mask transform available in Glue Studio. — Option C is correct because AWS Glue provides a built-in Mask transform that can be applied directly in Glue Studio or via the AWS Glue API. This transform is designed to reliably obfuscate sensitive data like credit card numbers by replacing them with a fixed string (e.g., 'XXXX-XXXX-XXXX-XXXX') or asterisks, ensuring complete masking regardless of input format. The custom Python Map transform in the current implementation is error-prone because it relies on a regular expression that may not catch all patterns or partial digits, whereas the Mask transform uses predefined logic to guarantee full masking.
What should I do if I get this DEA-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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Question Discussion
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