MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A team built a SageMaker Pipeline that includes a training step and a model evaluation step. They want to automatically register a model in SageMaker Model Registry only if the evaluation metric (accuracy) exceeds 0.9. Which pipeline step should be used to implement this conditional logic?
⚠ Common exam trap
A common trap is thinking the RegisterModel step itself can conditionally register a model based on metrics, but in SageMaker Pipelines, conditional logic must be implemented explicitly with a ConditionStep.
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 in SageMaker Pipelines allows you to add conditional branching logic, such as evaluating a metric and proceeding only if a condition is met. In this scenario, you would use a ConditionStep to check if the accuracy metric from the evaluation step exceeds 0.9, and then conditionally execute a RegisterModel step to register the model in SageMaker Model Registry. A common misconception is that the RegisterModel step itself can conditionally register a model based on metrics, but in SageMaker Pipelines, conditional logic must be implemented explicitly with a ConditionStep.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
RegisterModel step
Why it's wrong here
RegisterModel registers a model version unconditionally once reached; it holds no conditional logic to check accuracy against 0.9. It is tempting because registration is the desired outcome, but that would be correct only when the pipeline already guarantees the metric threshold upstream.
- ✓
Condition step
Why this is correct
A condition step evaluates a property such as the evaluation metric against a threshold and gates downstream steps accordingly. Placing the register step inside its true branch means registration occurs only when accuracy exceeds 0.9, exactly as the stem requires.
- ✗
Processing step
Why it's wrong here
A Processing step runs a containerised script over data or artefacts; it cannot branch pipeline execution on a condition. It is tempting because it can read evaluation output, but that would be correct for feature engineering or post-processing, not for gating registration with ConditionStep logic.
- ✗
Transform step
Why it's wrong here
A Transform step runs batch inference against a model and produces predictions; it cannot evaluate a metric or branch the pipeline. It is tempting because it consumes a trained model, which would be correct for generating batch predictions, not for conditionally registering a model.
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1 more way this is tested on MLA-C01
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Variation 1. 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?
medium- ✓ A.Condition step
- B.Processing step
- C.Transform step
- D.RegisterModel step
Why A: The Condition step evaluates a condition and branches the pipeline; if the condition is met, the pipeline proceeds to register the model.
JA
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Senior Network & Security Engineer · founder of Courseiva
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