The answer is SageMaker Pipelines with a conditional deployment step that includes a canary test. This is correct because SageMaker Pipelines natively supports conditional execution, allowing you to define a step that deploys the new model to a staging endpoint, runs a canary test to validate performance, and only promotes the model to production if the test passes. This eliminates manual intervention and directly addresses the need for automating model deployment with canary testing using SageMaker Pipelines, without requiring additional orchestration services like Step Functions. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of SageMaker Pipelines’ built-in condition nodes versus relying on external tools—a common trap is assuming you need Lambda or Step Functions for gating logic. Remember the mnemonic “Pipe, Canary, Promote”: Pipelines handle the flow, the canary step validates, and only then does promotion occur.
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. 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 uses SageMaker for training and inference. They have a model that retrains weekly. After each retraining, the model is evaluated on a held-out test set. If the evaluation metrics meet a threshold, the model is registered as 'Approved' in the SageMaker Model Registry. The team manually deploys the approved model to a production endpoint. They want to automate this deployment process to reduce manual errors. However, the deployment should only proceed if the new model passes a canary test in a staging environment. Which combination of AWS services should the team use to achieve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Pipelines with a conditional deployment step that includes a canary test.
SageMaker Pipelines natively supports conditional execution steps, allowing you to add a canary test step that evaluates the new model in a staging environment before automatically promoting it to production. This directly addresses the requirement for automated deployment gated by a canary test, without needing external orchestration services.
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 CodeDeploy with a blue/green deployment strategy.
Why it's wrong here
CodeDeploy is designed for EC2/ECS/Lambda deployments, not SageMaker endpoints.
✓
SageMaker Pipelines with a conditional deployment step that includes a canary test.
Why this is correct
Pipelines natively support conditional logic, canary deployments via weighted endpoints, and automatic rollback.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda to deploy to staging, then automatically promote to production if staging tests pass.
Why it's wrong here
Lambda requires custom code for canary testing and promotion, increasing complexity.
✗
Amazon EKS with a custom inference container and use ArgoCD for automated deployments.
Why it's wrong here
This approach moves away from SageMaker managed service, adding significant operational overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may overthink the solution and choose a generic CI/CD tool like CodeDeploy or Lambda, missing that SageMaker Pipelines already provides a fully managed, ML-specific orchestration with conditional deployment and canary testing capabilities.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps, including 'ConditionStep' that evaluates a boolean expression (e.g., canary test pass/fail) to decide whether to execute a 'DeployStep' or a 'FailStep'. The canary test can be implemented as a 'TransformStep' or 'ProcessingStep' that runs inference on a small subset of traffic and compares metrics against a threshold, all within the pipeline's execution history for auditability.
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: SageMaker Pipelines with a conditional deployment step that includes a canary test. — SageMaker Pipelines natively supports conditional execution steps, allowing you to add a canary test step that evaluates the new model in a staging environment before automatically promoting it to production. This directly addresses the requirement for automated deployment gated by a canary test, without needing external orchestration services.
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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Question Discussion
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