MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?
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
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Condition step
The Condition step in SageMaker Pipelines allows you to choose between two branches based on a condition. The other options are not designed for branching: Transform is for batch inference, Tuning is for hyperparameter optimization, and Processing is for data processing.
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
Condition step allows branching in the pipeline based on a Boolean condition.
- ✗
Transform step
Why it's wrong here
Transform step is for batch inference, not conditional branching.
- ✗
Processing step
Why it's wrong here
Processing step is for data processing and feature engineering.
- ✗
Tuning step
Why it's wrong here
Tuning step is for hyperparameter optimization jobs.
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