AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company wants to use AWS Lake Formation to govern access to data used for AI training. They need to ensure that only approved columns of sensitive tables are visible to data scientists. Which THREE steps should they implement? (Choose THREE)
⚠ Common exam trap
AIF-C01 often tests the steps for Lake Formation governance, and candidates may overlook the need to register the S3 bucket or confuse it with other services like SageMaker, leading to incorrect selections.
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 AWS Glue crawlers to catalog the data and populate the Data Catalog
The scenario requires governing access to AI training data with Lake Formation and restricting visibility to approved columns, so the solution must include cataloging, registering the data location, and applying column-level grants. Option A is correct because AWS Glue crawlers scan the S3 data, infer schemas, and populate the AWS Glue Data Catalog, which Lake Formation relies on to define and enforce permissions on tables and columns. Option C is correct because Lake Formation supports column-level permissions, allowing you to grant SELECT on only specific columns of a table to the data scientist role, which directly satisfies the requirement that only approved columns be visible. Option E is correct because the S3 bucket containing the training data must be registered with Lake Formation so that Lake Formation manages access to the underlying data and can enforce its table and column permissions. Option B is not required because a SageMaker notebook instance with an IAM role is a compute/access mechanism, not a governance step for restricting column visibility. Option D is not relevant because S3 versioning provides object version retention and recovery, not fine-grained column access control.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use AWS Glue crawlers to catalog the data and populate the Data Catalog
Why this is correct
AWS Glue crawlers scan the underlying data, infer schemas and register tables in the Data Catalog. Lake Formation permissions are granted against those catalogued tables and columns, so cataloguing is the prerequisite step enabling column-level access control.
- ✗
Create a SageMaker notebook instance and attach an IAM role
Why it's wrong here
A SageMaker notebook instance with an IAM role provides compute and identity for training, but IAM policies operate at table and S3 prefix level, not individual columns. It is tempting because notebooks are where data scientists consume data, but it would be correct when the task is provisioning an analysis environment rather than restricting column visibility.
- ✓
Define column-level permissions in Lake Formation to grant access to specific columns for the data scientist role
Why this is correct
Column-level grants in Lake Formation restrict which columns a role can query, so data scientists see only approved fields of sensitive tables. This satisfies the requirement that unapproved columns remain invisible, since Lake Formation enforces the restriction at query time.
- ✗
Enable S3 versioning on the training data bucket
Why it's wrong here
S3 versioning preserves object revisions; it grants no column-level access control, so data scientists could still read every column. It is tempting because versioning is a common data-protection step for training buckets, but it would be correct when the requirement is recoverability from overwrites or deletions, not column filtering.
- ✓
Register the S3 bucket containing the training data with Lake Formation
Why this is correct
Lake Formation governs permissions only over data it knows about, so the S3 bucket must first be registered as a data location. This establishes the catalogue metadata and IAM role access that column-level security filters then apply to, satisfying the requirement to expose only approved columns.
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 AIF-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 AIF-C01 exam.