MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?
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
✓
Condition step
The Condition step evaluates a condition and branches the pipeline; if the condition is met, the pipeline proceeds to register the model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Condition step
Why this is correct
A condition step evaluates a JSON condition against the evaluation step's output and branches execution accordingly, so registration only proceeds when metrics exceed the threshold. This satisfies the requirement for conditional model registration within SageMaker Pipelines, unlike a processing or callback step.
- ✗
Processing step
Why it's wrong here
A Processing step runs a container job and emits output artefacts; it cannot branch pipeline execution on a metric condition. It is tempting because evaluation itself is often a Processing step, but conditional logic requires a Condition step, which gates the subsequent RegisterModel step on the threshold.
- ✗
Transform step
Why it's wrong here
A transform step runs batch inference or data processing jobs; it cannot evaluate a condition and branch the pipeline. The correct step is a condition step, which inspects evaluation output and gates registration; transform steps suit generating predictions or preprocessing datasets.
- ✗
RegisterModel step
Why it's wrong here
RegisterModel creates a model package group entry; it performs no metric comparison, so it would register unconditionally. It is tempting because registration is the desired end state, but the Condition step must precede it to evaluate the evaluation step's metric and gate registration on the threshold.
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