Azure ML Hyperparameter Tuning: Random Sampling vs. Early Termination

DP-100 By islever Editorial Last updated

Short answer

A Yes

Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job. You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperparameters. Solution: Use the random sampling method over the hyperparameter space. Does the solution meet the goal?
  1. Yes Correct Answer
  2. No

Key Takeaways

The exam tests the distinction between sampling algorithms; the common trap is confusing 'random' with 'no optimization', whereas random sampling in Azure ML fully supports both continuous parameters and early termination policies like Bandit.

This question tests knowledge of Azure Machine Learning sweep job sampling methods, specifically whether random sampling supports early termination and continuous hyperparameters. The community consensus confirms that random sampling is a valid solution for these requirements.

Common mistake: Many candidates choose 'No' because they associate early termination exclusively with bandit-based strategies (like MedianStopping or Bandit) and assume random sampling only works for discrete values without performance monitoring.

Expert Analysis

Why the Answer Is Correct

Random sampling is a fundamental strategy in Azure Machine Learning sweep jobs that selects hyperparameter configurations randomly from the defined search space. According to Microsoft documentation, random sampling explicitly supports both discrete and continuous hyperparameters. Furthermore, it is compatible with early termination policies, allowing low-performing runs to be stopped automatically to save resources.

Why the Other Options Are Wrong

Selecting 'No' implies that random sampling cannot handle continuous variables or early stopping, which is factually incorrect. While Bandit policies are often used for early termination, they are not the only mechanism; random sampling is the underlying method that can be paired with these policies. Confusing the sampling method (how points are chosen) with the policy (when to stop) leads to this error.

Community Comment Notes

Comments [1], [2], and [3] correctly cite the official Microsoft Learn documentation confirming that random sampling supports discrete/continuous parameters and early termination. Comment [4] and [5] incorrectly suggest Bandit is required, likely confusing the sampling algorithm with the termination policy. It is crucial to remember that you use a sampling method (like Random) and apply a policy (like Bandit) together.

Official Reference

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2

This link opens the official vendor documentation in a new tab.

Exam Strategy

Memorize the capabilities of each sampling method (Random, Grid, Bayesian). Remember that Random and Bayesian support continuous parameters, while Grid does not. Also, note that all sampling methods can utilize early termination policies like Bandit.

Community Votes

A
71%
B
29%

71% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 5 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 5

  1. gunn_m 👍 2 Answered A
    Sorry. I did a mistake: Random sampling supports discrete and continuous hyperparameters. It supports early termination of low-performance jobs. Some users do an initial search with random sampling and then refine the search space to improve results. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
  2. D0ktor 👍 2 Answered A
    Random sampling supports discrete and continuous hyperparameters. It supports early termination of low-performance jobs. Some users do an initial search with random sampling and then refine the search space to improve results. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
  3. astone42 👍 1 Answered A
    "Random sampling supports discrete and continuous hyperparameters. It supports early termination of low-performance jobs. Some users do an initial search with random sampling and then refine the search space to improve results."
  4. jefimija 👍 1 Answered B
    it should be median stopping or bandit
  5. gunn_m 👍 1 Answered B
    You need to use Bandit for this

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