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Microsoft DP-100 Exam - Topic 2 Question 136 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 136
Topic #: 2
[All DP-100 Questions]

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 hyperpara meters.

Solution: Use the Bayesian sampling method over the hyperparameter space.

Does the solution meet the goal?

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Suggested Answer: A

by Britt at Jan 27, 2026, 12:48 AM

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Annice
11 days ago
I believe Bayesian sampling does allow for early termination, but I might be mixing it up with another method. I should double-check that.
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Lasandra
16 days ago
I remember practicing with sweep jobs, and I feel like Bayesian methods were mentioned as effective, but I can't recall if they specifically allow for continuous hyperparameters.
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Leota
22 days ago
I think Bayesian sampling is a good choice because it can adapt based on previous results, but I'm not entirely sure if it supports early termination.
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Rosio
27 days ago
The Bayesian sampling method, huh? I'm not sure I fully understand how that works compared to other options. I'll need to spend some time exploring the different sampling techniques and their pros and cons to feel confident in my approach here.
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Izetta
1 month ago
Bayesian sampling, got it. That should give me the flexibility I need to tune the hyperparameters efficiently. I'll make sure to read up on the specifics before the exam, but this solution seems like it meets the goal.
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Ashlee
1 month ago
Hmm, I'm not too familiar with the different sampling methods in Azure ML. The early termination and continuous hyperparameters requirement makes this a bit tricky. I'll need to research the Bayesian approach more to see if it's the best fit.
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Murray
1 month ago
The Bayesian sampling method sounds like a good approach to support early termination and continuous hyperparameters. I'll need to review the details on how it works, but it seems promising.
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