MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML engineer is designing a SageMaker Pipeline for model training and registration. They need to ensure that the pipeline can be re-run with different datasets without manual intervention, and that the steps are only re-executed if inputs have changed. Which THREE features should they configure? (Select THREE.)
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Enable step caching to reuse outputs when inputs are unchanged
Pipeline parameters allow passing different inputs. Step caching reuses step outputs when inputs are identical. Using Parameterized execution is synonymous with using parameters. Lineage tracking is not for skipping steps. Condition steps are for branching, not caching. Model Registry is for versioning.
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