AIF-C01 Guidelines for Responsible AI Practice Question
A city government uses an Amazon SageMaker model to score affordable-housing applications. To meet its responsible AI commitments, the IT team must ensure that every automated decision can be traced back to the exact model version and training dataset used, and that changes are reviewed before deployment. Which combination of AWS practices best provides this accountability?
⚠ Common exam trap
The trap here is equating any governance-adjacent control, such as encryption or scaling, with accountability, when only versioned lineage plus an approval gate provides traceability and review.
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
✓
Register model versions in the SageMaker Model Registry with approval status and lineage, and require manual approval before deployment
Accountability for automated decisions requires a durable record of what produced each outcome and a controlled release process. The SageMaker Model Registry captures versioned model packages with approval status and lineage to training data, and its approval workflow enforces review before deployment. Scaling, instance sizing, and encryption improve performance or confidentiality but do not deliver traceability or approval gating.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Register model versions in the SageMaker Model Registry with approval status and lineage, and require manual approval before deployment
Why this is correct
The SageMaker Model Registry stores versioned model packages with metadata, approval status, and lineage to the training job and dataset. Gating deployment on an approved status creates a documented review step and a traceable record linking each decision to a specific model version and its training data, which is exactly the accountability the city requires.
- ✗
Configure a SageMaker endpoint with a larger instance type to reduce inference latency for applicants
Why it's wrong here
Choosing a larger instance improves throughput and response time. It has no bearing on version tracking, data lineage, or deployment approval, so decisions still cannot be traced to a specific model and dataset. It is an operational tuning choice and does not meet the accountability requirement.
- ✗
Store application data in an Amazon S3 bucket encrypted with AWS KMS customer managed keys
Why it's wrong here
Encryption at rest protects the confidentiality of stored data but does not record model versions, link decisions to training datasets, or enforce a review before deployment. It is a security control, not an accountability mechanism, so it does not satisfy the traceability and approval obligations described.
- ✗
Enable automatic scaling on the SageMaker endpoint so it can handle fluctuating application volumes
Why it's wrong here
Automatic scaling adjusts instance count to match traffic and improves availability and cost. It provides no record of which model version or dataset produced a decision and introduces no review gate. It addresses performance, not the traceability and approval requirements the city's responsible AI commitments demand.
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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.