MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Model Registry to manage model versions. They have a cross-account deployment requirement: models approved in the development account must be deployed to a production account. Which approach is the MOST secure and recommended?
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
✓
Share the model package group from the development account to the production account using AWS RAM, then create a model version in the production account
Cross-account deployment can be achieved by sharing the model package across accounts using AWS Resource Access Manager (RAM) or by exporting the model artifact to an S3 bucket with appropriate cross-account permissions, then creating the model in the target account.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export the model from Model Registry to a tar.gz file and upload to the production account manually
Why it's wrong here
Manual uploads are error-prone and not scalable.
- ✗
Copy the model artifact to a public S3 bucket and then create the model in the production account
Why it's wrong here
Public S3 buckets are insecure; artifacts should be accessed via private cross-account permissions.
- ✗
Use a Lambda function in the development account to call CreateEndpoint in the production account using cross-account IAM roles
Why it's wrong here
A Lambda function calling `CreateEndpoint` directly across accounts bypasses SageMaker Model Registry’s approval-gating and version-tracking mechanisms, so the deployment cannot enforce that only approved model versions are promoted. This approach is tempting because it automates cross-account deployment via IAM roles, which would be correct for a simple infrastructure provisioning task that does not require registry-based approval workflows.
- ✓
Share the model package group from the development account to the production account using AWS RAM, then create a model version in the production account
Why this is correct
AWS Resource Access Manager allows sharing model packages across accounts securely, and then the production account can deploy.
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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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.