DP-100 · Question #339
DP-100 Question #339: Real Exam Question with Answer & Explanation
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Question
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes. The code implements Bandit termination policy with slack_factor set to 0.2 and a sweep job with max_concurrent_trials set to 10. You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?
Options
- ASet the value of max_concurrent_trials to 20.
- BSet the value of slack_factor of early_termination policy to 0.1.
- CSet the value of slack_factor of early_termination policy to 0.9.
- DSet the value of max_concurrent_trials to 4.
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