DEA-C01 Data Security and Governance Practice Question
A healthcare company uses AWS Lake Formation to manage access to a data lake in Amazon S3. The data lake contains a table with patient records, and the company needs to ensure that only users in the 'Cardiology' department can query columns containing sensitive information such as patient name and diagnosis. Other departments should be able to query non-sensitive columns like patient ID and visit date. The company wants to implement this with the least operational overhead. What should the data engineer do?
⚠ Common exam trap
The trap here is assuming that IAM policies can enforce column-level access, but IAM operates at the resource level and cannot restrict individual 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
✓
Use Lake Formation column-level security to grant SELECT on the sensitive columns only to the Cardiology department role, and grant SELECT on non-sensitive columns to all other department roles.
Lake Formation column-level security allows granular access control at the column level, enabling the company to grant SELECT on sensitive columns only to the Cardiology department while allowing other departments to query non-sensitive columns. This approach centralizes permission management and requires no data duplication or custom ETL, minimizing operational overhead. It is the intended solution for fine-grained access control in a data lake.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create an AWS Glue ETL job that reads the table, filters out sensitive columns based on the user's department, and writes the results to separate S3 buckets for each department. Grant each department access to its respective bucket.
Why it's wrong here
This approach duplicates data and requires maintaining separate ETL jobs and buckets, increasing operational overhead and storage costs. It also does not provide real-time access control; users could access the wrong bucket if permissions are misconfigured. Lake Formation column-level security is a simpler, centralized solution that avoids data duplication.
- ✗
Use AWS Identity and Access Management (IAM) policies to deny access to the sensitive columns for all departments except Cardiology. Attach these policies to the IAM roles used by each department.
Why it's wrong here
IAM policies cannot directly restrict access to specific columns in a table managed by Lake Formation or the Glue Data Catalog. IAM policies operate at the resource level (e.g., S3 buckets, Glue tables) but not at the column level. Lake Formation is the appropriate service for column-level security, and using IAM alone would not meet the requirement.
- ✓
Use Lake Formation column-level security to grant SELECT on the sensitive columns only to the Cardiology department role, and grant SELECT on non-sensitive columns to all other department roles.
Why this is correct
Lake Formation supports column-level permissions, allowing fine-grained access control. By granting SELECT on sensitive columns only to the Cardiology role and on non-sensitive columns to other roles, the engineer enforces the requirement with minimal overhead. Lake Formation manages the permissions centrally, and no additional ETL or view creation is needed. This is the most efficient and secure approach.
- ✗
Configure an Amazon Athena workgroup for each department and use Athena's column-level access control to restrict sensitive columns. Grant each department's users access to their workgroup.
Why it's wrong here
Athena does not natively support column-level access control; it relies on Lake Formation or IAM policies for fine-grained access. Workgroups are used for query isolation and cost control, not for column-level security. This approach would not enforce the required restrictions and adds unnecessary complexity.
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.