DEA-C01 Data Security and Governance Practice Question
A data engineer is using AWS Lake Formation to manage fine-grained access to a data lake in Amazon S3. The engineer grants a data analyst SELECT permission on a table but wants to ensure that the analyst cannot access columns containing sensitive data such as social security numbers. The table is registered in the AWS Glue Data Catalog. Which Lake Formation feature should the engineer use to restrict access to specific columns?
⚠ Common exam trap
The trap here is mixing up row-level and column-level security features in Lake Formation, or assuming that S3 or Glue policies can enforce column-level access.
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 security in Lake Formation permissions.
Lake Formation column-level security allows granting SELECT on a subset of columns, effectively hiding sensitive columns from unauthorized users. Row-level security filters rows, not columns. Glue Data Catalog policies and S3 bucket policies do not provide column-level granularity for query engines.
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 using filter expressions in Lake Formation.
Why it's wrong here
Row-level security in Lake Formation restricts which rows a user can see based on filter expressions, but it does not control access to specific columns. The requirement is to hide sensitive columns, not filter rows, so this feature does not address the need.
- ✗
S3 bucket policies with prefix-based restrictions.
Why it's wrong here
S3 bucket policies operate at the object or prefix level and cannot restrict access to individual columns within a table. Since the data is stored in files, column-level security must be enforced at the metadata layer, which Lake Formation provides.
- ✗
AWS Glue Data Catalog resource policies.
Why it's wrong here
AWS Glue Data Catalog resource policies control access to the catalog itself, such as which principals can create or alter tables. They do not provide fine-grained column-level access control for query engines. Lake Formation is the appropriate service for column-level permissions.
- ✓
Column-level security in Lake Formation permissions.
Why this is correct
Lake Formation supports column-level security by allowing you to grant or deny access to specific columns within a table. When granting SELECT on a table, you can exclude sensitive columns, ensuring the analyst can query only approved columns. This directly meets the requirement without duplicating data.
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
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