MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?
⚠ Common exam trap
MLA-C01 often tests the confusion between caching (skip unchanged steps) and parallelism (run steps concurrently) — candidates must match the optimization goal to the correct feature.
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
✓
Pipeline caching
SageMaker Pipelines caching stores the output of a step keyed by the step's inputs (code, data, hyperparameters, etc.). When a pipeline run executes and the inputs are unchanged, the cached output is reused and the step is skipped, avoiding redundant training. This directly addresses the requirement to skip training when input data hasn't changed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pipeline caching
Why this is correct
Pipeline caching reuses a step's outputs when its inputs, code and parameters are unchanged, so the training step is skipped entirely rather than re-executed. This directly satisfies the stem's requirement to avoid redundant training when input data has not changed, saving compute time and cost.
- ✗
Pipeline variable expressions
Why it's wrong here
Pipeline variable expressions pass execution-time values between steps; they cannot compare current input data against previous runs to decide whether to skip training. They are intended for parameterising pipeline definitions, such as injecting a processed dataset URI. Detecting unchanged data requires a caching or conditional-execution mechanism, not variable substitution.
- ✗
Model registry approval
Why it's wrong here
Model registry approval governs promotion of a trained model to a deployable status; it does not evaluate whether input data changed, so training still runs. It is the right control when gating model versions before production release. Skipping redundant training needs a step-caching feature that hashes inputs and reuses prior outputs.
- ✗
Step parallelism
Why it's wrong here
Step parallelism runs independent steps concurrently, which changes execution timing but never inspects data content, so unchanged inputs still trigger training. Caching is the feature that skips a step when its inputs and code are unchanged; parallelism suits shortening pipelines with independent branches.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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