- A
AWS Config, Amazon GuardDuty, and AWS Security Hub.
Why wrong: These are security monitoring services, not deployment governance.
- B
Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.
Why wrong: These are for building APIs and workflows, not ML governance.
- C
AWS Service Catalog, AWS KMS, and AWS CloudTrail.
Why wrong: Service Catalog can enforce templates but is not designed for ML model governance.
- D
AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.
Correct. SCPs enforce policies, CodePipeline orchestrates deployment, and Model Registry ensures only approved models are deployed.
- E
AWS CloudFormation StackSets, Amazon EventBridge, and AWS Lambda.
Why wrong: StackSets manage infrastructure across accounts but lack specific model approval integration.
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's ML pipeline runs in multiple AWS accounts (dev, test, prod). They want to enforce that only approved models from a central Model Registry can be deployed to the production account. Which combination of services is MOST appropriate to implement this governance?
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
AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.
Option D is correct because it combines AWS Organizations with SCPs to enforce cross-account deployment policies, AWS CodePipeline with cross-account actions to orchestrate the pipeline across dev/test/prod accounts, and SageMaker Model Registry with approval status to gate deployments to only approved models. This ensures that only models with an 'Approved' status in the central registry can be deployed to the production account, meeting the governance requirement.
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.
- ✗
AWS Config, Amazon GuardDuty, and AWS Security Hub.
Why it's wrong here
These are security monitoring services, not deployment governance.
- ✗
Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.
Why it's wrong here
These are for building APIs and workflows, not ML governance.
- ✗
AWS Service Catalog, AWS KMS, and AWS CloudTrail.
Why it's wrong here
Service Catalog can enforce templates but is not designed for ML model governance.
- ✓
AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.
Why this is correct
Correct. SCPs enforce policies, CodePipeline orchestrates deployment, and Model Registry ensures only approved models are deployed.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS CloudFormation StackSets, Amazon EventBridge, and AWS Lambda.
Why it's wrong here
StackSets manage infrastructure across accounts but lack specific model approval integration.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may choose monitoring-focused options like A or C, mistakenly thinking that detecting non-approved deployments is sufficient, when the question explicitly requires enforcement (prevention), which demands a combination of policy-based controls (SCPs) and approval-gated pipelines (CodePipeline + Model Registry).
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Registry stores model versions with an approval status (e.g., 'Approved', 'Rejected') that can be updated via the SageMaker API or console. AWS CodePipeline can use cross-account actions by assuming an IAM role in the target account, and SCPs in AWS Organizations can restrict actions like sagemaker:CreateEndpointConfig to only allow approved model ARNs. This pattern ensures that even if a pipeline attempts to deploy an unapproved model, the SCP blocks the API call at the organization level.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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ML Solution Monitoring, Maintenance and Security — study guide chapter
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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: AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status. — Option D is correct because it combines AWS Organizations with SCPs to enforce cross-account deployment policies, AWS CodePipeline with cross-account actions to orchestrate the pipeline across dev/test/prod accounts, and SageMaker Model Registry with approval status to gate deployments to only approved models. This ensures that only models with an 'Approved' status in the central registry can be deployed to the production account, meeting the governance requirement.
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.
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Last reviewed: Jul 4, 2026
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