Azure ML Sweep Job Sampling Methods and Early Termination

DP-100 By islever Editorial Last updated

Short answer

B No

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 grid sampling method over the hyperparameter space. Does the solution meet the goal?
  1. Yes
  2. No Correct Answer

Key Takeaways

The question tests the specific limitations of grid search; while it supports early termination, it is strictly limited to discrete parameter values, failing the 'continuous' requirement.

Grid sampling in Azure Machine Learning does not support continuous hyperparameters, making it unsuitable for this scenario. The community consensus confirms that only random or Bayesian sampling methods handle both early termination and continuous parameters effectively.

Common mistake: Choosing 'Yes' because candidates assume all standard sampling methods (Grid, Random, Bayesian) behave identically regarding early termination and parameter types, ignoring Grid's discrete-only constraint.

Expert Analysis

Why the Answer Is Correct

The proposed solution uses grid sampling, which iterates through a predefined list of values. By definition, a grid requires discrete points; it cannot represent or test continuous ranges (e.g., learning rates between 0.1 and 0.5 without specifying exact steps). Therefore, it fails the requirement to support continuous hyperparameters.

Why the Other Options Are Wrong

Selecting 'Yes' is incorrect because it overlooks the fundamental nature of grid search. While options like Random or Bayesian sampling do support early termination policies (like Bandit or Median Stopping), they also natively handle continuous distributions. Grid search is computationally inefficient for high-dimensional spaces and lacks the flexibility for continuous variables.

Community Comment Notes

Comment [3] correctly cites Microsoft documentation confirming that grid search supports only discrete parameters. Comment [2] reinforces this by distinguishing between early termination support (which Grid has) and continuous parameter support (which Grid lacks). These comments align with the official behavior of Azure ML sweep jobs.

Official Reference

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

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

Exam Strategy

Always verify if a hyperparameter is defined as continuous or discrete when selecting a sampling method. Remember: Grid = Discrete Only; Random/Bayesian = Continuous + Discrete + Early Termination.

Community Votes

B
100%

100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 3 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 3

  1. 0ea0482 👍 2
    It should be yes. Random and Grid sampling methods support early termination.
  2. VeraKo 👍 1
    The answer is No, because grid search supports early termination but does not support continuous hyperparameters. It supports only discrete ones https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters?view=azureml-api-2
  3. D0ktor 👍 1 Answered B
    No. Although Grid sampling supports early termination, does not support continuous parameters

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