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AIF-C01 Practice Question: A healthcare startup is deploying an AI system to…
A healthcare startup is deploying an AI system to assist in diagnosing skin conditions from images. They want to follow the NIST AI Risk Management Framework. Which THREE practices should they implement?
⚠ Common exam trap
AIF-C01 often tests the tendency to select technically plausible but irrelevant AWS services (Glacier, Lambda) as answers to responsible AI questions — candidates must map options to NIST AI RMF functions rather than to general AWS capabilities.
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 Amazon SageMaker to continuously monitor model performance and retrain as needed
Option A is correct because the NIST AI RMF's MEASURE and MANAGE functions require ongoing monitoring of model performance in production and retraining when drift or degradation occurs, and Amazon SageMaker Model Monitor plus retraining pipelines directly support this continuous risk-management lifecycle. Option B is correct because the MAP function calls for documenting intended purpose, performance characteristics, and known limitations; a model card is the standard artifact for this transparency and is especially critical in a clinical context. Option C is correct because the MANAGE function emphasizes human oversight of AI decisions, and a human-in-the-loop process for uncertain diagnoses ensures a qualified clinician reviews low-confidence outputs, reducing patient-safety risk. Option D is not required by the NIST AI RMF; long-term archival in S3 Glacier is a data-retention choice that does not by itself address AI risk management, and the framework does not mandate any specific storage class. Option E is also not a framework requirement; AWS Lambda serverless inference is an architectural deployment decision, and the NIST AI RMF is technology-agnostic, so it neither prescribes nor favors Lambda over other inference options.
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 Amazon SageMaker to continuously monitor model performance and retrain as needed
Why this is correct
Continuous monitoring with retraining directly supports the NIST AI RMF's MEASURE and MANAGE functions, which require ongoing performance tracking and risk treatment. For diagnostic imaging, drift detection and periodic retraining address the healthcare-specific risk of degraded accuracy, satisfying the framework's demand for iterative risk management.
- ✓
Document the model's intended use, performance, and limitations in a model card
Why this is correct
Model cards record intended use, performance metrics and known limitations, giving the transparency and documentation the NIST AI RMF's Map and Measure functions require. For diagnostic imaging, this satisfies the framework's demand for traceable, auditable model characteristics.
- ✓
Establish a human-in-the-loop process for uncertain diagnoses
Why this is correct
Human-in-the-loop review routes uncertain or low-confidence diagnoses to a clinician, providing the oversight and intervention NIST AI RMF governance expects. For skin-condition diagnosis, this satisfies the framework's requirement to manage high-stakes errors before they affect patients.
- ✗
Archive all training data in Amazon S3 Glacier for long-term retention
Why it's wrong here
Glacier is a storage-tier decision covering retention cost, not a NIST AI RMF practice such as documenting data provenance or assessing bias. It would be correct when the requirement is cheap long-term archival of infrequently accessed records, not risk management for a diagnostic model.
- ✗
Deploy the model on AWS Lambda for serverless inference
Why it's wrong here
Lambda is a deployment mechanism and addresses none of the NIST AI RMF functions of govern, map, measure and manage. It would be the right choice when the requirement is cost-efficient, event-driven inference scaling, not when demonstrating risk management practices for a clinical diagnostic model.
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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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
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