MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?
⚠ Common exam trap
Many candidates confuse caching with conditional branching or parallel execution, assuming that skipping steps via conditions or running steps in parallel will avoid recomputation, when in fact only caching directly reuses prior outputs based on input immutability.
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
Pipeline caching in SageMaker Pipelines automatically reuses the output of a step if its inputs (including parameters, data, and code) have not changed since the last successful execution. This avoids recomputation by comparing a hash of the step's dependencies against previous runs, making it the correct feature to prevent unnecessary reruns when input data remains identical.
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
Why this is correct
Caching stores step outputs and reuses them when inputs are identical, preventing unnecessary reruns.
- ✗
Use a Lambda step to check input changes
Why it's wrong here
While possible, this is a custom workaround; caching is the built-in feature designed for this purpose.
- ✗
Use a Conditional step to skip steps
Why it's wrong here
Conditional steps are for branching logic, not for avoiding recomputation of unchanged steps.
- ✗
Set the pipeline execution mode to 'Parallel'
Why it's wrong here
Parallel execution runs steps concurrently but does not reuse cached results.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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