MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to share a trained model across multiple AWS accounts for inference. The model is stored in a central account's S3 bucket and needs to be deployed in other accounts' SageMaker endpoints. What is the recommended approach?
⚠ Common exam trap
It's easy for candidates to confuse AWS RAM (which shares VPCs and subnets) with resource-based policies (which share IAM-accessible resources like SageMaker models), leading them to pick Option A, even though RAM cannot share S3 objects or SageMaker model resources.
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
✓
Attach a resource policy to the model in the central account allowing the other accounts' SageMaker service principals to access it
SageMaker allows you to attach a resource-based policy directly to the model resource in the central account, granting the SageMaker service principal from other accounts permission to call `sagemaker:CreateModel` and `sagemaker:CreateEndpointConfig` using the shared model. This approach avoids copying artifacts and leverages AWS Identity and Access Management (IAM) cross-account trust, where the central account's model policy explicitly allows the remote account's SageMaker service role to access the model and its underlying S3 objects.
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 AWS RAM to share the model artifact S3 bucket
Why it's wrong here
AWS RAM shares subnets or resources, but SageMaker models are not RAM-shareable.
- ✓
Attach a resource policy to the model in the central account allowing the other accounts' SageMaker service principals to access it
Why this is correct
Resource policies enable cross-account access without moving artifacts.
- ✗
Use SageMaker Model Registry with cross-account sharing enabled
Why it's wrong here
Model Registry does not natively support cross-account model deployment.
- ✗
Copy the model artifacts to each account's S3 bucket and create separate models
Why it's wrong here
Works but is not efficient; duplication of data and management overhead.
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 |
Go deeper
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.