MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Pipelines to automate their ML workflow. They want to ensure that pipeline steps are not re-executed if the inputs and parameters have not changed since the last successful run. Which THREE features can help achieve this? (Choose three.)
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
✓
Use a ConditionStep to skip steps if data is unchanged
Pipeline caching reuses step outputs when inputs/parameters are identical. SageMaker Experiments lineage can track previous run metadata. Using a ConditionStep with checksums can skip steps based on data content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a ConditionStep to skip steps if data is unchanged
Why this is correct
A ConditionStep can branch based on data checksums, skipping unnecessary steps.
- ✗
Deploy a real-time endpoint for data validation
Why it's wrong here
Real-time endpoints are not relevant to pipeline step optimization.
- ✗
Use SageMaker Model Monitor
Why it's wrong here
Model Monitor is for production monitoring, not pipeline optimization.
- ✓
Enable pipeline caching on each step
Why this is correct
Caching avoids re-execution if step configuration hasn't changed.
- ✓
Leverage SageMaker Experiments lineage to compare input checksums
Why this is correct
Lineage tracking can store metadata about previous runs for comparison.
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