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MLA-C01 Practice Question: Which TWO SageMaker Pipelines steps are essential…
Which TWO SageMaker Pipelines steps are essential for automating a complete ML workflow from data processing to model deployment? (Choose 2.)
⚠ Common exam trap
AWS often tests the misconception that hyperparameter tuning or conditional logic are mandatory for a complete ML workflow, when in fact the minimal essential steps are data processing and model creation/deployment.
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
✓
A ProcessingStep to run data preprocessing and feature engineering.
Option B (ProcessingStep) is correct because it is the pipeline step that runs a SageMaker Processing job to execute data preprocessing, feature engineering, and dataset splitting, which is the required starting point for an end-to-end workflow from raw data to a deployable model. Option D (CreateModelStep or RegisterModelStep) is correct because it produces the SageMaker model artifact needed to complete the workflow: CreateModelStep packages the model for deployment, while RegisterModelStep records it in the Model Registry for governed deployment, thereby covering the model deployment end of the pipeline. Option A (TuningStep) is not essential here because hyperparameter tuning is an optional optimization stage, not a required step for a complete processing-to-deployment workflow. Option C (TransformStep) is not essential because batch inference on training data is an evaluation/inference activity, not a required stage for automating training and deployment. Option E (ConditionStep) is not essential because conditional branching on data quality is an optional control-flow enhancement rather than a mandatory step in a basic end-to-end pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A TuningStep for hyperparameter tuning.
Why it's wrong here
A TuningStep launches hyperparameter tuning jobs and selects the best model, but it neither preprocesses data nor deploys the trained model to an endpoint. It is tempting because tuning is a recognised pipeline step, yet the stem requires the processing and deployment steps that complete the end-to-end workflow.
- ✓
A ProcessingStep to run data preprocessing and feature engineering.
Why this is correct
A ProcessingStep executes the data preprocessing and feature engineering container, transforming raw input into the training-ready dataset. This satisfies the stem's requirement to automate the workflow's data processing stage within SageMaker Pipelines, feeding curated features downstream to training.
- ✗
A TransformStep for batch inference on the training data.
Why it's wrong here
A TransformStep runs batch inference against an existing model, which is not required to move data through processing, training, evaluation and deployment. It is tempting because batch scoring is a real pipeline activity, and it would be correct when scheduled inference over accumulated data is part of the workflow.
- ✓
A CreateModelStep (or RegisterModelStep) to register or deploy the trained model.
Why this is correct
CreateModelStep packages the trained model artefacts into a deployable SageMaker model, while RegisterModelStep records it in the Model Registry. Either satisfies the stem's deployment stage, enabling the pipeline to move from training output to a registered or deployable model.
- ✗
A ConditionStep to decide whether to train a model based on data quality.
Why it's wrong here
A ConditionStep only branches pipeline execution on a boolean property, such as a data-quality metric, so it cannot process data or deploy a model itself. It is tempting because conditional logic genuinely belongs in pipelines for gating retraining, but the stem asks for the steps that perform the workflow's core processing and deployment.
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