- A
SageMaker Pipelines
SageMaker Pipelines provides DAG-based orchestration with all the required step types and automatic lineage tracking.
- B
Amazon MWAA (Managed Workflows for Apache Airflow)
Why wrong: MWAA is generic orchestrator; not SageMaker-native and requires more custom integration.
- C
AWS Glue workflows
Why wrong: Glue workflows are for ETL, not full ML pipelines with tuning and conditional deployment.
- D
AWS Step Functions with Lambda functions for each step
Why wrong: Step Functions can orchestrate but lacks native SageMaker integration for tuning, training, and lineage compared to Pipelines.
Question 264 of 1,000
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 team wants to orchestrate a multi-step ML workflow that includes data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment to staging or production based on evaluation metrics. The workflow should run on a schedule and track lineage. Which service should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Pipelines
SageMaker Pipelines is the correct choice because it is purpose-built for orchestrating multi-step ML workflows, including data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment. It natively supports scheduling via EventBridge or a cron expression, tracks lineage automatically through SageMaker Experiments and artifact tracking, and allows conditional branching (e.g., deploy to staging or production based on evaluation metrics) using `ConditionStep`.
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
Why this is correct
SageMaker Pipelines provides DAG-based orchestration with all the required step types and automatic lineage tracking.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon MWAA (Managed Workflows for Apache Airflow)
Why it's wrong here
MWAA is generic orchestrator; not SageMaker-native and requires more custom integration.
- ✗
AWS Glue workflows
Why it's wrong here
Glue workflows are for ETL, not full ML pipelines with tuning and conditional deployment.
- ✗
AWS Step Functions with Lambda functions for each step
Why it's wrong here
Step Functions can orchestrate but lacks native SageMaker integration for tuning, training, and lineage compared to Pipelines.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AWS Step Functions or MWAA because they are familiar general-purpose orchestrators, but they overlook that SageMaker Pipelines is the only service that provides native, end-to-end ML workflow orchestration with built-in lineage tracking, conditional deployment, and direct integration with SageMaker training, tuning, and model registry.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps, where each step can be a `TrainingStep`, `TuningStep`, `ProcessingStep`, `ConditionStep`, or `CreateModelStep`, and it automatically captures metadata such as input/output artifacts, parameters, and metrics in SageMaker Experiments. Under the hood, it leverages Amazon SageMaker Model Registry for versioning and lineage, and the `ConditionStep` evaluates a boolean expression (e.g., `Accuracy > 0.9`) to route the workflow to staging or production deployment. In a real-world scenario, a team might schedule the pipeline to run nightly, retrain a model, and only deploy to production if the evaluation metric exceeds a threshold, with full traceability of data, code, and model versions.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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 — SageMaker Pipelines is the correct choice because it is purpose-built for orchestrating multi-step ML workflows, including data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment. It natively supports scheduling via EventBridge or a cron expression, tracks lineage automatically through SageMaker Experiments and artifact tracking, and allows conditional branching (e.g., deploy to staging or production based on evaluation metrics) using `ConditionStep`.
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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Last reviewed: Jul 4, 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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