Quick Answer
The answer is Amazon SageMaker Model Registry, along with Amazon SageMaker Pipelines and Amazon CloudWatch, as these three services together enable automated CI/CD pipeline for ML model deployment on SageMaker with automatic rollback capabilities. SageMaker Model Registry provides a centralized catalog for versioning and approving models, allowing the pipeline to deploy only approved versions and revert to a previous one when performance degrades, as detected by CloudWatch metrics monitoring. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of MLOps automation patterns, often appearing as a multi-select question where you must distinguish between services like SageMaker Model Monitor (which only detects drift, not rollback) and the Registry’s approval workflow. A common trap is choosing SageMaker Model Monitor alone, but remember: monitoring detects degradation, while the Registry enables the actual rollback. Memory tip: “Registry for rollback, Monitor for metrics, Pipelines for process.”
This MLA-C01 practice question tests your understanding of ml model development. 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.
An MLOps team is designing a CI/CD pipeline for deploying machine learning models to production on Amazon SageMaker. They want to ensure that the deployment process is automated and that models are automatically rolled back if performance degrades. Which of the following AWS services or features should they use to achieve this? (Choose THREE.)
Amazon SageMaker Model Registry
Model Registry manages model versions and approvals.
Amazon SageMaker Ground Truth
Why wrong: Ground Truth is for data labeling.
Amazon CloudWatch
CloudWatch monitors performance and triggers alerts.
Amazon SageMaker Pipelines
Pipelines automate the deployment workflow.
AWS CloudTrail
Why wrong: CloudTrail is for auditing API calls.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon SageMaker Model Registry
Amazon SageMaker Model Registry is correct because it provides a centralized catalog for managing, versioning, and approving ML models. It enables automated deployment by triggering CI/CD pipelines when a model version is approved, and supports automatic rollback by allowing you to revert to a previous approved version if performance degrades, as detected by monitoring metrics.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon SageMaker Model Registry
Why this is correct
Model Registry manages model versions and approvals.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Ground Truth
Why it's wrong here
Ground Truth is for data labeling.
Amazon CloudWatch
Why this is correct
CloudWatch monitors performance and triggers alerts.
Related concept
Read the scenario before looking for a memorised answer.
Amazon SageMaker Pipelines
Why this is correct
Pipelines automate the deployment workflow.
Related concept
Read the scenario before looking for a memorised answer.
AWS CloudTrail
Why it's wrong here
CloudTrail is for auditing API calls.
Common exam traps
The trap here is that candidates may confuse SageMaker Ground Truth (a data labeling service) or CloudTrail (an auditing service) with the core MLOps components needed for automated deployment and rollback, overlooking that Model Registry, Pipelines, and CloudWatch are the precise services that form the CI/CD and monitoring backbone.
Detailed technical explanation
Under the hood, SageMaker Model Registry integrates with SageMaker Pipelines to create a fully automated MLOps workflow. When a model is registered and approved, a pipeline can be triggered to deploy it to a SageMaker endpoint. CloudWatch alarms monitor endpoint metrics like latency or error rate; if a metric breaches a threshold, a Lambda function can invoke a rollback by updating the endpoint to use a previous model version from the registry. This pattern is critical in production environments where model drift or data shifts can cause sudden performance drops.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Model Registry — Amazon SageMaker Model Registry is correct because it provides a centralized catalog for managing, versioning, and approving ML models. It enables automated deployment by triggering CI/CD pipelines when a model version is approved, and supports automatic rollback by allowing you to revert to a previous approved version if performance degrades, as detected by monitoring metrics.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
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 team uses SageMaker Experiments to track multiple training runs. They need to register the best-performing model in the model registry for approval. Which method ensures the model artifacts and metadata are captured correctly?
easyWhy C: Option D is correct because SageMaker Model Registry provides a centralized catalog for model versions with associated metadata, metrics, and approval status. Option A is wrong because manual comparison is error-prone. Option B is wrong because Experiments track runs but do not natively register models. Option C is wrong because Lambda is not a direct mechanism for model registration.
Last reviewed: Jun 24, 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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