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Microsoft DP-100 Exam - Topic 4 Question 121 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 121
Topic #: 4
[All DP-100 Questions]

You use Azure Machine Learning to train a model.

You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples.

You need to select a sampling method.

Which sampling method should you use?

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Suggested Answer: B

by Clay at Jan 26, 2025, 07:10 AM

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Maybelle
3 months ago
I thought grid search was still popular for hyperparameter tuning?
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Laurel
3 months ago
Wait, are we sure Bayesian is the best choice?
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Luisa
3 months ago
Random sampling? Nah, that won't cut it here.
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Annice
4 months ago
Grid search is too rigid for this.
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Tammy
4 months ago
Definitely go with Bayesian! It uses past performance to guide sampling.
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Francoise
4 months ago
I’m leaning towards Bayesian too, but I wonder if there are cases where Grid might actually work better.
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Lauran
4 months ago
I practiced a similar question, and I think Random sampling doesn’t really consider past performance, so it might not be the right choice here.
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Alease
5 months ago
I’m not entirely sure, but I feel like Grid search is more systematic, while Bayesian could be more efficient for tuning.
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Jannette
5 months ago
I remember studying different sampling methods, and I think Bayesian sampling is the one that adapts based on previous performance.
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Derrick
5 months ago
I think Bayesian sampling is the best choice for this scenario. It's designed to intelligently explore the hyperparameter space and converge on the optimal settings, which is exactly what we need to do here.
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Desmond
5 months ago
Random search might be a good option here. It's simple to implement and can sometimes find good hyperparameters without making too many assumptions about the problem.
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Princess
5 months ago
Bayesian sampling sounds like the way to go. It should be able to efficiently explore the hyperparameter space and zero in on the optimal settings based on the model's performance.
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Salome
5 months ago
I'm a bit unsure about this one. I know grid search and random search are common methods, but I'm not as familiar with Bayesian sampling. I'll need to review the differences between them to make a confident decision.
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Vinnie
5 months ago
I think the Bayesian sampling method would be the best choice here. It's designed to optimize the hyperparameters based on the performance of previous samples.
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Vince
10 months ago
If I choose random sampling, I might as well just throw darts at the answer choices and hope for the best. Bayesian is the way to go, hands down.
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Margurite
8 months ago
B) Bayesian
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Giuseppe
9 months ago
A) Grid
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Dorian
10 months ago
Bayesian sampling is the clear winner here. It's like having a crystal ball that tells you which samples to try next. Grid and random sampling are so last century.
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Rodrigo
8 months ago
User 3: Grid and random sampling are outdated compared to Bayesian sampling.
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Kenny
8 months ago
User 2: I agree, Bayesian sampling is like having a crystal ball for picking the right samples.
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Isidra
9 months ago
User 1: I think we should use Bayesian sampling for tuning hyperparameters.
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Jonelle
10 months ago
Grid sampling might work, but it's not the most efficient method for tuning hyperparameters. Bayesian is the way to go if you want to optimize your model's performance.
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Barbra
9 months ago
Random sampling might not be as effective as Bayesian for this task.
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Lon
9 months ago
I agree, Bayesian sampling is more efficient for tuning hyperparameters.
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Anastacia
10 months ago
I think Bayesian sampling would be the best choice.
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Arlette
11 months ago
I'm not sure. Grid sampling is also a valid option, as it exhaustively searches through all possible combinations.
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Myong
11 months ago
Random sampling? Really? That doesn't sound like it would be very effective for tuning hyperparameters. I'm going with Bayesian on this one.
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Boris
10 months ago
Random sampling might not be the most effective method for this task.
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Filiberto
10 months ago
I agree, Bayesian sampling is a better choice for tuning hyperparameters.
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Sanjuana
11 months ago
I agree with Gracie. Bayesian sampling considers previous samples, which can lead to better hyperparameter tuning.
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Gracie
11 months ago
I think we should use Bayesian sampling.
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Merissa
11 months ago
I think Bayesian sampling would be the best choice here. It's designed to pick samples based on previous performance, which is exactly what the question asks for.
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Bulah
9 months ago
I would go with Bayesian sampling for sure.
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Jade
9 months ago
Bayesian sampling is definitely the right option for this scenario.
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Xenia
9 months ago
I think Bayesian sampling is the most suitable choice.
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Raymon
10 months ago
I agree, Bayesian sampling is the way to go.
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