Azure ML Sweep Job Sampling Methods and Early Termination
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?
Community Votes
100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
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.
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.
Community Discussion (3 comments)
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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
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.