Exam AI-300 Topic 1 Question 34 Discussion
Actual exam question for Microsoft's AI-300 exam
Question #: 34
Topic #: 1
Question #: 34
Topic #: 1
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 slackjactor 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?
The code implements Bandit termination policy with slackjactor 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?
Suggested Answer: C Vote an answer
To improve the sampling convergence and effectiveness of your Bayesian hyperparameter sweep, you must decrease the value of max_concurrent_trials (for example, setting max_concurrent_trials = 4).
Decrease max_concurrent_trials
Bayesian sampling is a fundamentally sequential algorithm. It relies entirely on the outcomes of previously completed trials to calculate and select the next optimized set of hyperparameters.
The Problem: Setting max_concurrent_trials = 10 forces the optimization engine to kick off 10 trials at the exact same time. Because none of these 10 overlapping trials have completed or generated metrics yet, they cannot learn from one another. This high parallelism compromises the intelligence of the algorithm, turning your structured Bayesian sweep into a less effective random sampling process.
The Solution: Reducing max_concurrent_trials to a smaller number (such as 4) allows sets of trials to complete sequentially. This ensures the optimizer captures enough historical performance data to intelligently direct subsequent runs, achieving significantly better convergence.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
Decrease max_concurrent_trials
Bayesian sampling is a fundamentally sequential algorithm. It relies entirely on the outcomes of previously completed trials to calculate and select the next optimized set of hyperparameters.
The Problem: Setting max_concurrent_trials = 10 forces the optimization engine to kick off 10 trials at the exact same time. Because none of these 10 overlapping trials have completed or generated metrics yet, they cannot learn from one another. This high parallelism compromises the intelligence of the algorithm, turning your structured Bayesian sweep into a less effective random sampling process.
The Solution: Reducing max_concurrent_trials to a smaller number (such as 4) allows sets of trials to complete sequentially. This ensures the optimizer captures enough historical performance data to intelligently direct subsequent runs, achieving significantly better convergence.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
by Perry at Oct 08, 2026, 05:53 AM
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