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
Pipeline caching stores step outputs keyed on input signatures, so unchanged data and code let SageMaker skip re-execution and reuse prior artefacts. This directly removes the unnecessary recomputation the company observes, cutting cost and runtime without altering pipeline structure.
- ✗
Use a Lambda step to check input changes
Why it's wrong here
A Lambda step only inspects inputs and returns a signal; it cannot suppress downstream step execution, so recomputation continues. It is tempting because Lambda suits lightweight custom checks, and would be right for branching logic or validation, but caching requires SageMaker's step caching, keyed on input signatures.
- ✗
Use a Conditional step to skip steps
Why it's wrong here
Conditional steps branch on a property's value, but they do not compare input data against previous runs, so unchanged inputs still trigger recomputation. They are tempting for skipping steps based on explicit conditions, and would be correct when a pipeline must choose paths dynamically rather than reuse cached outputs.
- ✗
Set the pipeline execution mode to 'Parallel'
Why it's wrong here
Parallel execution mode runs independent branches concurrently; it changes scheduling, not whether steps recompute, so unchanged inputs still rerun. It is tempting because parallel mode shortens wall-clock time, and would be correct for pipelines with independent branches, whereas avoiding recomputation requires caching keyed on input signatures.
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