DEA-C01 Data Security and Governance Practice Question
A company uses AWS Lake Formation to manage data lakes on Amazon S3. The data engineer needs to grant a data analyst access to query specific columns in a table using Amazon Athena, but deny access to columns containing personally identifiable information (PII). Which Lake Formation feature should be used?
⚠ Common exam trap
DEA-C01 often tests the confusion between row-level filters (horizontal) and column-level permissions (vertical), and candidates may incorrectly choose tag-based access control when the question asks for the specific column-restriction feature.
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
✓
Column-level permissions in Lake Formation.
Lake Formation column-level permissions allow a data engineer to grant SELECT on specific columns of a table while excluding others, which is exactly what is needed to hide PII columns from the analyst. When the analyst queries via Athena, Lake Formation enforces the column filter at query time, returning only the permitted columns. This is the native, fine-grained access control mechanism for column-level restrictions in Lake Formation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Row-level security filters.
Why it's wrong here
Row-level security filters restrict which rows a principal sees, leaving every column readable, so PII columns remain exposed. They are the right feature when the requirement is to hide records, such as restricting a region's analyst to that region's rows.
- ✓
Column-level permissions in Lake Formation.
Why this is correct
Column-level permissions in Lake Formation restrict access at the individual column granularity, so the analyst can query non-PII columns while PII columns remain denied. This satisfies the stem's requirement to grant selective column access through Athena without exposing sensitive data, since Lake Formation enforces these controls centrally across integrated analytics services.
- ✗
Tag-based access control with Lake Formation tags.
Why it's wrong here
Tags control resource access, not column-level granularity.
- ✗
Cell-level security with AWS Glue.
Why it's wrong here
Cell-level security with AWS Glue filters individual cell values, not whole columns, so it cannot deny the PII columns outright. It is tempting because cell-level security genuinely restricts sensitive values within rows, and would be right when only some records in a column are PII. Here, column-level security in Lake Formation is required.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.