DEA-C01 Data Security and Governance Practice Question
A data engineer manages an AWS Glue Data Catalog table that contains sensitive customer PII. The table's underlying data is in Amazon S3 and is queried by several AWS analytics services. The security team wants to implement column-level access control so that only authorized principals can view the PII columns, while other principals can still query non-sensitive columns. The solution must integrate with AWS Lake Formation and be enforced consistently across all query engines. Which approach should the data engineer take?
⚠ Common exam trap
The trap here is assuming that IAM policies or Glue Data Catalog resource policies can enforce column-level access control, but they operate at different levels and cannot restrict specific columns within a table.
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
✓
Register the S3 bucket as a Lake Formation data location, then grant column-level permissions on the table using Lake Formation.
Lake Formation column-level permissions are designed to provide fine-grained access control on tabular data in the Data Catalog. By registering the S3 location and granting column-level permissions, the data engineer can restrict access to sensitive columns while allowing queries on other columns. This enforcement is consistent across integrated analytics services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement an S3 bucket policy that allows only certain prefixes and use separate buckets for PII and non-PII data.
Why it's wrong here
S3 bucket policies control access at the bucket or prefix level, not at the column level within a table. Separating data into different buckets based on sensitivity is a data organization strategy, but it does not provide column-level access control within a single table and does not integrate with Lake Formation's fine-grained permissions.
- ✗
Create an IAM policy that denies access to the PII columns and attach it to the roles used by the analytics services.
Why it's wrong here
IAM policies operate at the API action level for services like S3, but they cannot enforce column-level restrictions within a table. IAM policies cannot distinguish between columns when querying via Athena or Redshift Spectrum. Therefore, this approach fails to provide the required granularity and consistency.
- ✗
Use AWS Glue Data Catalog resource policies to restrict access to specific columns in the table.
Why it's wrong here
AWS Glue Data Catalog resource policies control access to the catalog objects (databases, tables) but do not support column-level permissions. They can restrict who can access the catalog, but once a principal has table access, they can read all columns. This does not satisfy the need for column-level access control across query engines.
- ✓
Register the S3 bucket as a Lake Formation data location, then grant column-level permissions on the table using Lake Formation.
Why this is correct
Lake Formation column-level permissions allow granular control over specific columns in a table. By registering the S3 location and granting column-level permissions, the engineer ensures that only authorized principals can access PII columns, and this enforcement is applied across integrated services like Athena, Redshift Spectrum, and Glue ETL. This directly meets the requirement for consistent column-level access control.
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.