hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML team uses SageMaker Pipelines to automate…
An ML team uses SageMaker Pipelines to automate retraining. After a pipeline failure, they need to reprocess only the failed step without rerunning the entire pipeline. What should they do?
⚠ Common exam trap
It's easy for candidates to confuse SageMaker Pipelines Cache with Model Monitor's drift detection, assuming that monitoring automatically handles retraining failures, when in fact caching is the correct mechanism for step-level reuse.
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 SageMaker Pipelines Cache with step-level caching.
SageMaker Pipelines Cache with step-level caching allows you to reuse outputs from previous successful runs of unchanged steps. When a pipeline fails, only the failed step and any downstream steps that depend on it need to be re-executed, because cached results from prior successful steps are automatically retrieved. This avoids rerunning the entire pipeline, saving time and compute resources.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a new pipeline version for each run.
Why it's wrong here
Creating new versions does not help with reprocessing a single step.
- ✗
Use SageMaker Model Monitor to detect drift and trigger retraining.
Why it's wrong here
Model Monitor is for monitoring inference quality, not for pipeline step recovery.
- ✓
Use SageMaker Pipelines Cache with step-level caching.
Why this is correct
Caching enables the pipeline to skip completed steps and resume from the failed step.
- ✗
Manually rerun the pipeline with updated parameters.
Why it's wrong here
Manual rerun would repeat all steps, wasting time and resources.
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