MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is building a pipeline using Amazon SageMaker Pipelines. The pipeline has multiple steps including data preprocessing, training, and evaluation. Which statement about SageMaker Pipelines is correct?
⚠ Common exam trap
A common mix-up: candidates assume pipelines are strictly sequential (like traditional scripts) and overlook SageMaker's support for parallelism, custom code, and conditional logic, leading them to select option A or D.
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
✓
Pipelines support caching of step outputs.
SageMaker Pipelines supports output caching, which allows step outputs to be reused when the step configuration and inputs remain unchanged. This caching mechanism reduces execution time and cost by skipping redundant computations for steps like data preprocessing or training when their parameters have not changed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Steps in a pipeline must run sequentially.
Why it's wrong here
They can run in parallel.
- ✓
Pipelines support caching of step outputs.
Why this is correct
Caching speeds up re-runs.
- ✗
Pipelines can only use built-in algorithms.
Why it's wrong here
Custom scripts are allowed.
- ✗
Pipelines cannot have conditional branches.
Why it's wrong here
Conditional execution is supported.
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