AIF-C01 Practice Question: Security, Compliance, and Governance for AI Solutions
A company has trained a model using Amazon SageMaker and stored the model artifacts in S3 with SSE-KMS encryption. The development team wants to grant cross-account access to the model artifacts so a partner can deploy the model in their own account. Which steps are required?
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
✓
Create an S3 bucket policy allowing the partner account to read the objects, and ensure the KMS key policy allows the partner account to use the key
To share SSE-KMS encrypted objects cross-account, you must grant the partner account access to both the S3 object (via bucket policy) and the KMS key (via key policy). The partner must also have the correct IAM permissions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a new IAM role in the partner account with S3 read permissions
Why it's wrong here
A partner-side IAM role alone grants nothing: the bucket policy must also allow that principal, and the KMS key policy must permit decrypt. It is tempting because creating a role in the consuming account is genuinely part of cross-account access, but only alongside resource-based policies.
- ✓
Create an S3 bucket policy allowing the partner account to read the objects, and ensure the KMS key policy allows the partner account to use the key
Why this is correct
Cross-account access requires permissions at both layers: the S3 bucket policy grants the partner account read access to the objects, while the SSE-KMS key policy must separately allow that account to decrypt using the customer managed key. Missing either blocks access.
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Use AWS Lake Formation to share the data
Why it's wrong here
Lake Formation governs data lakes and tables, not individual S3 objects under SSE-KMS; it cannot authorise decryption of model artifacts. It is tempting because Lake Formation does legitimately manage cross-account data sharing, which is the correct tool when sharing catalogued tabular datasets rather than artefacts.
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Copy the model artifacts to a public S3 bucket
Why it's wrong here
A public bucket exposes the artifacts to everyone and still fails, since SSE-KMS ciphertext requires key policy permission to decrypt. It is tempting because public access does technically let the partner download objects, but it breaches least privilege and bypasses the KMS authorisation the scenario requires.
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.