AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A data scientist needs to train a model in Amazon SageMaker using a dataset that contains personally identifiable information (PII). The company policy requires all data at rest to be encrypted with a customer-managed key. Which configuration meets this requirement?
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
✓
Specify a KMS customer-managed key when creating the SageMaker training job and enable data encryption for the S3 bucket with the same key
Using a KMS customer-managed key (CMK) for SageMaker's EBS volumes and S3 buckets ensures encryption at rest with a key the customer controls. The other options either use AWS-managed keys, skip encryption, or are not applicable to SageMaker training jobs.
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 default encryption for the S3 bucket using AWS-managed S3 keys (SSE-S3)
Why it's wrong here
SSE-S3 uses AWS-managed keys, so the customer cannot control key rotation or revocation, failing the customer-managed key policy. It is tempting because it is the default, zero-configuration encryption for S3 buckets, and would be correct where the requirement is simply encryption at rest without key-ownership constraints.
- ✓
Specify a KMS customer-managed key when creating the SageMaker training job and enable data encryption for the S3 bucket with the same key
Why this is correct
Specifying a KMS customer-managed key for the training job and encrypting the source S3 bucket with the same key satisfies the policy that all data at rest uses a customer-managed key. SageMaker's default encryption uses AWS-owned keys, which the policy excludes.
- ✗
Encrypt the data before uploading to S3 using a client-side library
Why it's wrong here
Client-side encryption protects the object before upload, but the SageMaker training job and its volumes remain unencrypted with a customer-managed key, so the policy is unmet. It is tempting because it gives the customer full control of the data, and would be correct where only the stored dataset, not the training infrastructure, falls in scope.
- ✗
Use SageMaker's local mode and store data on the instance's ephemeral storage
Why it's wrong here
Ephemeral instance storage is not encrypted at rest with a customer-managed KMS key, and local mode still reads the dataset from S3, so the policy is unmet. It is tempting for rapid iteration on small samples, and would be correct where the requirement is debugging training code locally without provisioning managed infrastructure.
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.