Quick Answer
Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for orchestrating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services like AWS CodePipeline, which is better suited for CI/CD of application code rather than ML step orchestration. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between general-purpose orchestration tools and ML-specific services; a common trap is choosing AWS CodePipeline because it is a familiar CI/CD service, but it lacks native SageMaker step types and Model Registry integration. Remember the memory tip: “Pipelines for ML steps, CodePipeline for app deploys”—if the workflow involves SageMaker training, evaluation, or model registration, always reach for SageMaker Pipelines first.
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 wants to automate its machine learning pipeline using AWS CodePipeline and Amazon SageMaker. The pipeline should train a model, evaluate it, and if the evaluation passes, register the model in the SageMaker Model Registry. Which service should the company use to orchestrate the training and evaluation steps?
AWS CodePipeline
Why wrong: CodePipeline is for CI/CD, not for orchestrating training steps.
AWS Glue Workflows
Why wrong: Glue workflows are for ETL, not ML training.
AWS Step Functions
Why wrong: Step Functions can orchestrate but requires extra configuration compared to Pipelines.
Amazon SageMaker Pipelines
SageMaker Pipelines natively supports ML steps like training, evaluation, and model registration.
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 Pipelines
Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.
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.
AWS CodePipeline
Why it's wrong here
CodePipeline is for CI/CD, not for orchestrating training steps.
AWS Glue Workflows
Why it's wrong here
Glue workflows are for ETL, not ML training.
AWS Step Functions
Why it's wrong here
Step Functions can orchestrate but requires extra configuration compared to Pipelines.
Amazon SageMaker Pipelines
Why this is correct
SageMaker Pipelines natively supports ML steps like training, evaluation, and model registration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may confuse AWS Step Functions (a general-purpose orchestrator) with SageMaker Pipelines (a specialized ML orchestrator), overlooking that SageMaker Pipelines provides built-in SageMaker step types and native Model Registry integration, which Step Functions lacks without custom Lambda functions.
Detailed technical explanation
SageMaker Pipelines uses a pipeline definition file (JSON or YAML) that defines each step as a SageMaker job (e.g., TrainingStep, ProcessingStep, ConditionStep) and automatically handles artifact lineage, parameter passing, and retry logic. Under the hood, each step is executed as a separate SageMaker job, and the ConditionStep evaluates a metric (e.g., accuracy threshold) to decide whether to proceed to a RegisterModel step, which creates a new model version in the Model Registry. In a real-world scenario, this allows teams to version both the pipeline and the model artifacts, enabling reproducible ML workflows that can be triggered by new data or code changes.
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon SageMaker Pipelines — Amazon SageMaker Pipelines is the correct choice because it is a purpose-built, fully managed service for creating end-to-end machine learning workflows directly within the SageMaker ecosystem. It natively integrates with SageMaker training jobs, processing jobs for evaluation, and the Model Registry for conditional registration, allowing the entire pipeline—train, evaluate, and conditionally register—to be defined as a directed acyclic graph (DAG) of steps without needing to stitch together separate services.
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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Same concept, more angles
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Variation 1. A data scientist wants to automate retraining of a model weekly and deploy the new model automatically after passing validation. Which AWS service combination is best?
easyWhy A: SageMaker Pipelines orchestrates the ML workflow including training and validation, and Step Functions can trigger deployment. SageMaker alone lacks native scheduling, and Lambda cannot orchestrate complex workflows.
Variation 2. A team wants to automate the retraining and deployment of an ML model whenever new labeled data arrives in S3. The workflow includes data preprocessing, training, evaluation, and conditional deployment. Which AWS service is best suited for orchestrating this end-to-end pipeline?
easyWhy D: Amazon SageMaker Pipelines is purpose-built for ML workflows, offering native integration with SageMaker for training, evaluation, and conditional deployment steps. Triggered by S3 events via Amazon EventBridge, it automates the end-to-end pipeline from data preprocessing to conditional model deployment without requiring custom orchestration code.
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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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