AI-300 ML Model Lifecycle And Operations Practice Question
You are orchestrating a multi-step ML pipeline in Azure Machine Learning. You need to ensure that a downstream step only executes if the upstream model training step finishes successfully, while allowing the pipeline to continue even if a non-critical logging step fails. Which configuration should you use?
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
✓
Configure pipeline run settings with 'continue_on_step_failure' set to True for the logging step.
You should set the 'continue_on_step_failure' property to True for non-critical steps and ensure dependencies are defined via 'StepRun' output objects.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure a 'WaitStep' to monitor the training job status.
Why it's wrong here
WaitStep is not a standard Azure ML pipeline component.
- ✓
Configure pipeline run settings with 'continue_on_step_failure' set to True for the logging step.
Why this is correct
This allows the pipeline to proceed if the specific step fails.
- ✗
Set the 'PipelineParameter' to execute only on success.
Why it's wrong here
PipelineParameters are for inputs, not flow control.
- ✗
Use an 'Estimator' class with 'allow_reuse' set to False.
Why it's wrong here
Allow_reuse is for caching, not error handling.
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Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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