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Actual exam question for Microsoft's AI-300 exam Question #: 171 Topic #: 1
Hotspot Question You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy for an Azure ML Python SDK v2-based model training. The policy uses a slack_factor set to 0.1, an evaluation interval set to 1, and an evaluation delay set to 5. You need to evaluate the outcome of the early termination policy. What should you evaluate? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Suggested Answer:
Explanation: Box 1: 91% Box 2: Every interval when metrics are reported, starting at evaluation interval 5 Every interval when metrics are reported, starting at evaluation interval 5 is the best description for the run termination schedule in this scenario. evaluation_interval = 1: The policy checks for potential termination every time the training script logs the primary metric. evaluation_delay = 5: Policy evaluation is suspended for the first 5 intervals to prevent the premature termination of training runs before they have time to stabilize. Combined Behavior: Once the evaluation_delay threshold of 5 is met, the policy applies at every subsequent multiple of the evaluation_interval (Intervals 5, 6, 7, etc.). Reference: https://azure.github.io/azureml-sdk-for-r/reference/bandit_policy.html
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