The answer is to create a SageMaker Pipeline that includes a model approval step and a deployment step. This approach is correct because a SageMaker Pipeline can orchestrate the entire end-to-end workflow, from retraining to automatically deploying the latest approved model from SageMaker Model Registry, by using a conditional approval gate that only triggers deployment when the model status is “Approved.” On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of MLOps automation and the integration between SageMaker Pipelines and Model Registry; a common trap is to choose a Lambda-based trigger or a manual approval workflow, which lack the built-in orchestration and approval gating that a Pipeline provides. Remember the key concept: a Pipeline is the single, unified orchestrator that ties retraining, approval, and deployment together. A useful memory tip is “Pipeline gates the gatekeeper”—the Pipeline itself acts as the gatekeeper, ensuring only the approved model passes through to production.
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 company is using SageMaker Model Registry to manage model versions. They want to automatically deploy the latest approved model to production after retraining. Which approach is best?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a SageMaker Pipeline that includes a model approval step and deployment step
Option C is correct because a SageMaker Pipeline can orchestrate the entire workflow from retraining to deployment, including a model approval step that gates deployment to production only when the model is approved. This automates the process end-to-end, ensuring that only approved models are deployed, which aligns with the requirement to automatically deploy the latest approved model after retraining.
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.
✗
Manually deploy the approved model using the SageMaker console
Why it's wrong here
Manual deployment is not automatic and is error-prone.
✗
Use AWS Lambda to update the endpoint whenever a new model version is created
Why it's wrong here
Lambda would need to check approval status and can become complex; not best practice.
✓
Create a SageMaker Pipeline that includes a model approval step and deployment step
Why this is correct
SageMaker Pipelines can model the entire workflow including conditional deployment based on approval.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Schedule a CloudWatch Event to invoke a SageMaker update endpoint API daily
Why it's wrong here
This would not consider approval status and may deploy unapproved models.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may choose Option B because it sounds automated, but they overlook the critical requirement for model approval before deployment, which Lambda alone cannot enforce without additional logic.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Pipeline uses a directed acyclic graph (DAG) of steps, where a Model Approval step can be implemented using a Lambda step or a custom step that checks the model version's approval status in the Model Registry. The deployment step then uses a CreateEndpointConfig and UpdateEndpoint API call to update the production endpoint only when the model is marked as 'Approved'. This ensures that the deployment is tightly coupled to the approval workflow, avoiding race conditions or manual errors.
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: Create a SageMaker Pipeline that includes a model approval step and deployment step — Option C is correct because a SageMaker Pipeline can orchestrate the entire workflow from retraining to deployment, including a model approval step that gates deployment to production only when the model is approved. This automates the process end-to-end, ensuring that only approved models are deployed, which aligns with the requirement to automatically deploy the latest approved model after retraining.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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