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 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.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Pipelines to orchestrate training and evaluation steps.
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.
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.
✓
SageMaker Pipelines to orchestrate training and evaluation steps.
Why this is correct
Pipelines define the sequence of steps and conditional logic for retraining and evaluation.
Related concept
Read the scenario before looking for a memorised answer.
✓
Amazon S3 bucket to store training data and model artifacts.
Why this is correct
S3 provides durable storage for inputs and outputs of the pipeline.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon CloudWatch to log API calls.
Why it's wrong here
CloudWatch is useful for monitoring but not essential for the pipeline logic.
✓
SageMaker Model Registry to store and version models.
Why this is correct
Model Registry enables tracking, approval, and deployment of model versions.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda function to trigger evaluation.
Why it's wrong here
Lambda is not essential; the pipeline can use built-in evaluation steps.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse monitoring services like CloudWatch with essential pipeline components, or assume that a serverless function like Lambda is required for evaluation when SageMaker Pipelines already provides native evaluation capabilities.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps, such as TrainingStep, ProcessingStep, and ConditionStep, to automate model retraining and evaluation. The Model Registry integrates with these pipelines to automatically register approved models, enabling version control and deployment to endpoints via CodePipeline. In a real-world scenario, a pipeline might use a ConditionStep to compare a new model's evaluation metric against a threshold, and only deploy if it exceeds the current production model's performance.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 to orchestrate training and evaluation steps. — 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.
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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