AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company needs to govern the lifecycle of ML models, including versioning, monitoring for drift, and decommissioning outdated models. Which TWO services should they use? (Choose 2)
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
✓
Amazon SageMaker Model Registry
Amazon SageMaker Model Registry (B) is correct because it provides a centralized catalog for versioning ML models, tracking approval status, and managing the model lifecycle through metadata such as model packages and model groups, which directly supports versioning and decommissioning outdated models. Amazon SageMaker Model Monitor (C) is correct because it continuously monitors deployed models for data drift, model quality drift, bias drift, and feature attribution drift, alerting when behavior deviates from the baseline, which fulfills the drift-monitoring requirement. AWS CloudTrail (A) only records API activity for auditing and governance of account actions, not ML model versioning or drift detection. Amazon S3 (D) is object storage for artifacts and data, not a lifecycle governance or monitoring service. AWS CodePipeline (E) is a CI/CD orchestration service for automating build and deployment stages, not for model registry or drift monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail records API activity for audit and compliance, giving an activity log rather than model versioning, drift detection or decommissioning. It is the right choice when the requirement is auditing who invoked which API and when, not governing the model lifecycle.
- ✓
Amazon SageMaker Model Registry
Why this is correct
Amazon SageMaker Model Registry provides a centralised catalogue for versioning ML models, tracking approval status, and managing their lifecycle through to decommissioning. It satisfies the stem's governance requirements by recording model metadata and lineage, while drift monitoring is handled by SageMaker Model Monitor alongside it.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
Amazon SageMaker Model Monitor continuously captures endpoint inference data and compares it against baselines, detecting data drift and model quality degradation. This directly satisfies the stem's monitoring-for-drift requirement within the ML lifecycle, complementing a model registry for versioning and decommissioning.
- ✗
Amazon S3
Why it's wrong here
Amazon S3 stores objects and artefacts, but provides no model registry, drift detection or lifecycle governance. It is tempting as the storage layer beneath SageMaker, yet the requirement needs Model Registry and Model Monitor to version, track drift and retire models.
- ✗
AWS CodePipeline
Why it's wrong here
CodePipeline orchestrates CI/CD build, test and deploy stages for code and model artefacts; it holds no model registry, lineage or drift detection capability. It is the correct choice when the requirement is automating retraining and deployment workflows, not governing model versions and monitoring.
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 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.