MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)
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
✓
Training step
For caching to avoid redundant runs, the steps that produce outputs that can be reused must have caching enabled. The processing step (evaluation) and training step both generate outputs that can be cached if their inputs (code, data, hyperparameters) remain the same. The condition step does not produce outputs to cache; it just branches. The RegisterModel step typically registers metadata, but its inputs (model artifact, metrics) may be generated by previous steps; enabling caching on the RegisterModel step can also avoid re-running if the same model artifact is already registered.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Training step
Why this is correct
Training outputs a model artifact; caching avoids retraining if inputs unchanged.
- ✗
Transform step (if used)
Why it's wrong here
Not all pipelines include a transform step; caching on Transform is possible but not required for the described scenario.
- ✓
Processing step (evaluation)
Why this is correct
Processing evaluation produces metrics; caching avoids re-evaluation if inputs unchanged.
- ✗
Condition step
Why it's wrong here
Condition step is a logic branch; it does not produce cacheable outputs.
- ✓
RegisterModel step
Why this is correct
RegisterModel can cache if the input model artifact and metadata are unchanged, preventing redundant registration.
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