MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?
⚠ Common exam trap
A common mix-up: candidates think caching requires external logic (like a Lambda step) or scheduling, when SageMaker Pipelines has a native `CacheConfig` property that directly addresses redundant runs with minimal configuration.
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
✓
Enable pipeline caching by setting the CacheConfig property for each step
SageMaker Pipelines supports step caching via the `CacheConfig` property. When enabled, the pipeline checks if the step's inputs (including source data, parameters, and code) have changed since the last successful run. If no changes are detected, the step is skipped and the previous output is reused, eliminating redundant compute.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable pipeline caching by setting the CacheConfig property for each step
Why this is correct
Caching causes the pipeline to skip steps if inputs and configuration haven't changed, saving time and cost.
- ✗
Configure the pipeline to run on a schedule instead of on-demand
Why it's wrong here
Scheduling does not affect step redundancy; it still runs all steps each time.
- ✗
Use the Parameter step to pass previous execution ID
Why it's wrong here
Parameters do not prevent re-execution; they just pass values.
- ✗
Use Lambda step to check data changes before running
Why it's wrong here
Using a Lambda step introduces custom logic outside SageMaker Pipelines' native capabilities for detecting input data changes. While Lambda is excellent for bespoke pre-processing, external API calls, or complex conditional logic, SageMaker Pipelines possess built-in mechanisms to cache step outputs and skip re-execution if upstream data or code remains unchanged. Implementing a manual check via Lambda would bypass this inherent optimisation, adding unnecessary complexity and cost. It would be suitable for checks against systems not natively integrated or for highly specific, non-standard conditions.
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