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?
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 step outputs; if the step configuration and inputs are identical, the pipeline reuses the cached output, skipping execution.
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
Caching compares step hash and skips execution if unchanged.
- ✗
Pipeline variable expressions
Why it's wrong here
Pipeline variables parameterize pipelines but do not skip steps based on unchanged inputs.
- ✗
Model registry approval
Why it's wrong here
Model registry manages model versions and approvals but does not skip pipeline steps.
- ✗
Step parallelism
Why it's wrong here
Parallelism runs steps concurrently but does not skip steps.
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