DEA-C01 Data Security and Governance Practice Question
A company is using AWS Lake Formation to manage access to a data lake in S3. They want to grant a data analyst access to specific columns in a table, but not to the entire table. Which Lake Formation feature should be used?
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 filtering
Lake Formation column-level filtering allows granting access to specific columns in a table without granting access to the entire table. Option A (row-level security) controls access to rows, not columns. Option B (IAM policies on the S3 bucket) would grant access to the entire dataset or bucket, not specific columns. Option D (tag-based access control) uses tags to manage permissions but does not provide column-level granularity.
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 (cell-level filtering)
Why it's wrong here
Row-level security filters which rows a principal sees, not which columns, so it cannot grant access to specific columns while hiding others. It is tempting because it also implements fine-grained Lake Formation permissions, and would be correct when restricting an analyst to rows matching a filter predicate such as region.
- ✗
IAM policies on the S3 bucket
Why it's wrong here
S3 bucket IAM policies govern object-level access to prefixes and objects, not column-level filtering within a Lake Formation table, so they cannot restrict an analyst to specific columns. They are tempting for broad data lake permissions, and would be correct when controlling access to whole S3 prefixes or objects.
- ✓
Column-level filtering
Why this is correct
Column-level filtering in Lake Formation applies column-level permissions on a table, letting the analyst query only the granted columns while excluded columns are hidden. This satisfies the stem's requirement to restrict access to specific columns rather than the entire table.
- ✗
Tag-based access control (TBAC)
Why it's wrong here
Tag-based access control grants permissions by matching LF-Tags on resources and principals, not by naming individual columns in a table grant. It is tempting because it scales governance across many tables, and would be the correct choice when classifying data assets and granting access by category rather than column.
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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.