- A
Use AWS RAM to share the model artifact S3 bucket
Why wrong: AWS RAM shares subnets or resources, but SageMaker models are not RAM-shareable.
- B
Attach a resource policy to the model in the central account allowing the other accounts' SageMaker service principals to access it
Resource policies enable cross-account access without moving artifacts.
- C
Use SageMaker Model Registry with cross-account sharing enabled
Why wrong: Model Registry does not natively support cross-account model deployment.
- D
Copy the model artifacts to each account's S3 bucket and create separate models
Why wrong: Works but is not efficient; duplication of data and management overhead.
Question 492 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, when you attach a resource policy to a SageMaker model, you specify a `Principal` that matches the remote account's SageMaker service role ARN (e.g., `arn:aws:iam::REMOTE_ACCOUNT_ID:role/service-role/AmazonSageMaker-ExecutionRole`). The policy must also grant `sagemaker:CreateModel` on the model ARN and `s3:GetObject` on the S3 artifact bucket. A subtle behavior is that the remote account's SageMaker service role must have a trust policy allowing `sagemaker.amazonaws.com` to assume it, and the S3 bucket policy must allow the remote role's access to the artifact objects. In real-world scenarios, this pattern is critical for multi-account MLOps pipelines where a central data science team trains models and deploys them to production accounts without duplicating storage.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Attach a resource policy to the model in the central account allowing the other accounts' SageMaker service principals to access it — Option B is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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