The answer is a condition step, the SageMaker Model Registry, and a model evaluation step. This combination is correct because a CI/CD pipeline for ML with conditional deployment requires a mechanism to gate the release based on performance thresholds; the evaluation step computes the metrics, the condition step checks whether those metrics exceed the defined threshold, and the Model Registry stores the approved model version, ensuring only validated artifacts proceed to deployment. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of how to enforce governance in automated ML workflows—a common trap is to include a deployment step directly after training, forgetting that the condition must be evaluated before registry approval. A useful memory tip is “Evaluate, Condition, Register”: the pipeline must first evaluate the model, then check the condition, and finally register the approved version before deployment can trigger.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning team is building a CI/CD pipeline to train and deploy models using Amazon SageMaker. They want to ensure that the deployment step only proceeds if the model evaluation metrics exceed a certain threshold. Which THREE components should the team include in the pipeline? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The SageMaker Model Registry to approve and store the model after evaluation.
Option C is correct because the SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.
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.
✗
An AWS Lambda function for manual approval.
Why it's wrong here
Manual approval is not required; the condition can be automated.
✗
An AWS CodeBuild project to compile the model artifacts.
Why it's wrong here
CodeBuild is not necessary; SageMaker handles training.
✓
The SageMaker Model Registry to approve and store the model after evaluation.
Why this is correct
Model Registry can store model versions and track approval status.
Related concept
Read the scenario before looking for a memorised answer.
✓
A SageMaker endpoint deployment step that runs only after approval.
Why this is correct
Deployment step is required to create or update the endpoint.
Related concept
Read the scenario before looking for a memorised answer.
✓
A condition step that checks if the evaluation metric exceeds the threshold.
Why this is correct
The condition step gates the deployment based on metric values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that manual approval via Lambda is required for gating deployments, but the correct approach uses SageMaker Model Registry's built-in approval mechanism combined with a condition step in the pipeline.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Model Registry integrates with AWS CodePipeline via the SageMaker Model Building Pipeline, where a condition step evaluates metrics (e.g., accuracy, F1 score) against a threshold using SageMaker Processing or built-in evaluation jobs. If the condition passes, the model version is automatically registered with an 'Approved' status, and the deployment step (e.g., creating a SageMaker endpoint) is triggered only for approved versions, ensuring a fully automated, gated release process.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The SageMaker Model Registry to approve and store the model after evaluation. — Option C is correct because the SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.
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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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 is building a CI/CD pipeline for ML models using AWS CodePipeline and SageMaker. The pipeline should include steps to automatically retrain, evaluate, and deploy models. Which THREE components are essential for this pipeline? (Choose three.)
hard
✓ A.SageMaker Pipelines to orchestrate training and evaluation steps.
✓ B.Amazon S3 bucket to store training data and model artifacts.
C.Amazon CloudWatch to log API calls.
✓ D.SageMaker Model Registry to store and version models.
E.AWS Lambda function to trigger evaluation.
Why A: SageMaker Pipelines is essential because it provides a native orchestration service to define, automate, and manage the end-to-end ML workflow, including training, evaluation, and conditional deployment steps. It integrates directly with other SageMaker components and CodePipeline, enabling a seamless CI/CD pipeline without requiring custom orchestration logic.
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Question Discussion
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