MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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`.
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.
- ✗
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.
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