- A
Use AWS Lake Formation to define column-level permissions for sensitive data.
Lake Formation provides column-level security to mask sensitive columns.
- B
Configure AWS Glue Data Catalog to automatically mask sensitive columns in table definitions.
Why wrong: Glue Data Catalog does not support column masking; that is a Lake Formation feature.
- C
Organize data in S3 using a partition structure like 'year=YYYY/month=MM/day=DD/region=XX/'.
Partitioning improves query performance by pruning partitions.
- D
Use S3 object tags to label sensitive data and apply bucket policies to restrict access.
Why wrong: S3 object tags are metadata and do not enforce column-level masking.
- E
Implement S3 lifecycle policies to transition sensitive data to S3 Glacier after 30 days.
Why wrong: Lifecycle policies manage storage tiers, not access control.
Quick Answer
The correct answer involves two actions: organizing data in S3 using a partition structure like year=YYYY/month=MM/day=DD/region=XX and using AWS Lake Formation to mask sensitive columns. Partitioning by date and region is essential because it allows AWS Glue ETL jobs to use partition pruning, drastically reducing the amount of data scanned and improving query performance and cost efficiency. For masking sensitive columns, Lake Formation provides fine-grained column-level permissions and data filtering that integrate directly with the Glue Data Catalog, enabling you to restrict or obfuscate PII for specific IAM roles without modifying the underlying S3 data. On the AWS Certified Data Engineer Associate DEA-C01 exam, this tests your understanding of combining storage optimization with security governance—a common trap is thinking S3 bucket policies alone can mask columns, but they cannot; only Lake Formation or Athena workgroups can. Memory tip: think “Partition for performance, Lake Formation for protection.”
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. 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 engineer is designing a data lake on Amazon S3 that will be accessed by multiple AWS Glue ETL jobs. The engineer needs to ensure that the data is organized efficiently for querying and that sensitive columns are masked for certain users. Which TWO actions should the engineer take? (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
Use AWS Lake Formation to define column-level permissions for sensitive data.
Option A is correct because AWS Lake Formation provides fine-grained access control at the column level, allowing you to mask or restrict sensitive columns (e.g., PII) for specific IAM roles or users without altering the underlying data in S3. This is achieved through Lake Formation’s column-level permissions and data filtering, which integrate directly with the AWS Glue Data Catalog and query engines like Athena and Redshift Spectrum.
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 AWS Lake Formation to define column-level permissions for sensitive data.
Why this is correct
Lake Formation provides column-level security to mask sensitive columns.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure AWS Glue Data Catalog to automatically mask sensitive columns in table definitions.
Why it's wrong here
Glue Data Catalog does not support column masking; that is a Lake Formation feature.
- ✓
Organize data in S3 using a partition structure like 'year=YYYY/month=MM/day=DD/region=XX/'.
Why this is correct
Partitioning improves query performance by pruning partitions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use S3 object tags to label sensitive data and apply bucket policies to restrict access.
Why it's wrong here
S3 object tags are metadata and do not enforce column-level masking.
- ✗
Implement S3 lifecycle policies to transition sensitive data to S3 Glacier after 30 days.
Why it's wrong here
Lifecycle policies manage storage tiers, not access control.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse S3 object tags or bucket policies with fine-grained column-level access control, or assume the Glue Data Catalog can natively mask columns, when in fact only Lake Formation provides that capability.
Detailed technical explanation
How to think about this question
Lake Formation’s column-level security works by attaching a resource-based policy to the Data Catalog table that defines which columns are accessible or masked for specific principals; this policy is enforced during query planning in Athena or Redshift Spectrum, not at the S3 layer. Partitioning with a structure like 'year=YYYY/month=MM/day=DD/region=XX' leverages Hive-style partitioning, which allows Glue ETL jobs and query engines to prune partitions efficiently, reducing scan costs and improving performance. A common real-world scenario is a healthcare data lake where patient names must be hidden from analysts but visible to data stewards, requiring both column-level masking and efficient partitioning for daily ingestion.
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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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Lake Formation to define column-level permissions for sensitive data. — Option A is correct because AWS Lake Formation provides fine-grained access control at the column level, allowing you to mask or restrict sensitive columns (e.g., PII) for specific IAM roles or users without altering the underlying data in S3. This is achieved through Lake Formation’s column-level permissions and data filtering, which integrate directly with the AWS Glue Data Catalog and query engines like Athena and Redshift Spectrum.
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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Last reviewed: Jun 24, 2026
This DEA-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 DEA-C01 exam.
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