hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data science team is using Amazon SageMaker…
A data science team is using Amazon SageMaker Pipelines to orchestrate a multi-step workflow that includes data preprocessing, training, and model evaluation. They want to reuse the preprocessed data across multiple pipeline executions without re-running the preprocessing step if the source data hasn't changed. What should they configure?
⚠ Common exam trap
A common mix-up: candidates confuse checkpointing (for training resumption) with caching (for step reuse), or assume that Feature Store or Data Wrangler inherently provide caching, when in fact only Processing steps with explicit CacheConfig enable this behavior in SageMaker Pipelines.
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 Processing steps with caching
SageMaker Processing steps support caching, which allows the pipeline to skip re-execution of the preprocessing step if the input data and pipeline parameters have not changed. This is achieved by configuring a `CacheConfig` with a caching key based on the input data source and step parameters, ensuring that the preprocessed data is reused across multiple pipeline executions without redundant computation.
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 SageMaker Training steps with checkpointing
Why it's wrong here
Checkpointing is for resuming training jobs, not for caching preprocessing outputs.
- ✓
Use SageMaker Processing steps with caching
Why this is correct
Caching in SageMaker Pipelines reuses step outputs when inputs are identical, avoiding redundant computation.
- ✗
Use SageMaker Feature Store to store the preprocessed features
Why it's wrong here
Feature Store is for online feature serving, not for caching intermediate pipeline step outputs.
- ✗
Use SageMaker Data Wrangler for the preprocessing
Why it's wrong here
Data Wrangler creates preprocessing flows but caching is handled by Pipelines, not Data Wrangler itself.
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