- 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.
Quick Answer
The answer is AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status. This combination enforces multi-account governance for SageMaker models by using SCPs to block unauthorized deployment actions in the production account, CodePipeline to orchestrate the cross-account promotion of only approved model versions, and the Model Registry’s approval gate to ensure that only validated models proceed. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to combine identity-based policies with service-level approval workflows to separate duties across environments. A common trap is to suggest IAM roles alone, which lack the centralized enforcement that SCPs provide at the organization level. Remember the mnemonic “OSCAR” — Organizations, SCPs, CodePipeline, Approval, Registry — to recall the key services for governing model deployments across accounts.
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.
AWS Organizations SCPs restrict actions, CodePipeline automates cross-account deployment, and Model Registry provides approval gates.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
- ✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
- →
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 — Static NAT maps one inside address to one outside address..
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. — AWS Organizations SCPs restrict actions, CodePipeline automates cross-account deployment, and Model Registry provides approval gates.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company operates multiple AWS accounts with SageMaker workloads. They need to implement governance and security controls for model monitoring and maintenance. Which THREE actions should they take to meet compliance requirements?
hard- ✓ A.Deploy a SageMaker model registry in a centralized account.
- ✓ B.Use AWS CloudTrail to log all API calls to SageMaker and S3.
- C.Enable VPC Flow Logs for SageMaker notebooks.
- D.Use IAM roles with cross-account trust policies for all SageMaker endpoints.
- ✓ E.Use AWS Config rules to enforce encryption of model artifacts.
Why A: CloudTrail logging, AWS Config rules for encryption, and a centralized model registry help enforce governance across accounts.
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Last reviewed: Jun 23, 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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