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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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Merissa
3 days ago
Bayesian sampling? More like Bayesian sorcery! But seriously, this is the right call for the job.
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Nan
9 days ago
Bayesian sampling is the solution I would have chosen too. It's the best option for this scenario.
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Pa
14 days ago
Bayesian sampling is the way to go. It's like having a crystal ball for your hyperparameters. Nailed it!
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Desmond
19 days ago
Agreed, the Bayesian sampling method is perfect for this use case. It's efficient and can handle the requirements nicely.
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Allene
24 days ago
Bayesian sampling is definitely the way to go for early termination and continuous hyperparameter tuning. This solution is spot on.
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Kirby
29 days ago
I think the question is tricky; I remember that some sampling methods don't support continuous parameters, but I thought Bayesian was one that did.
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Annice
2 months 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
2 months 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
2 months 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
2 months 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
2 months 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
3 months 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
3 months 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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