hardMultiple SelectObjective-mapped
AIF-C01 Practice Question: A financial services company uses Amazon…
A financial services company uses Amazon SageMaker Feature Store to manage features for machine learning models. The compliance auditor requires that all changes to feature definitions are logged and that feature data is immutable once written. Which TWO approaches should the team implement? (Choose two.)
⚠ Common exam trap
Watch out — candidates often confuse AWS Config (which tracks resource configuration changes) with AWS CloudTrail (which logs API calls), or they mistakenly think the offline store's point-in-time query capability inherently enforces data immutability, when in fact immutability requires explicit design choices.
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
✓
Enable AWS CloudTrail for SageMaker Feature Store API calls.
Enabling AWS CloudTrail for SageMaker Feature Store API calls provides a detailed audit log of all operations, including changes to feature definitions (e.g., CreateFeatureGroup, UpdateFeatureGroup). This satisfies the compliance requirement for logging all changes. Option C is correct because enabling feature group versioning in SageMaker Feature Store allows you to track and manage changes to feature definitions over time, ensuring a historical record of modifications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable AWS CloudTrail for SageMaker Feature Store API calls.
Why this is correct
CloudTrail logs all API calls, providing an audit trail for changes.
- ✗
Use SageMaker Feature Store offline store with record identifier and event time.
Why it's wrong here
This helps with time-based queries but does not inherently log definition changes.
- ✓
Enable feature group versioning to track changes to feature definitions.
Why this is correct
Versioning tracks changes and provides immutability for definitions.
- ✗
Implement feature store online store with TTL to automatically expire data.
Why it's wrong here
TTL manages data lifecycle, not immutability or change logging.
- ✗
Use AWS Config to track changes to Feature Store resources.
Why it's wrong here
Config tracks resource configurations, not specific feature definition changes.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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