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Exam DP-100 topic 3 question 114 discussion

Actual exam question from Microsoft's DP-100
Question #: 114
Topic #: 3
[All DP-100 Questions]

You are implementing hyperparameter tuning by using Bayesian sampling for a model training from a notebook. The notebook is in an Azure Machine Learning workspace that uses a compute cluster with 20 nodes.

The code implements Bandit termination policy with slack factor set to 0.2 and the HyperDriveConfig class instance with max_concurrent_runs set to 10.

You must increase effectiveness of the tuning process by improving sampling convergence.

You need to select which sampling convergence to use.

What should you select?

  • A. Set the value of slack factor of early_termination_policy to 09.
  • B. Set the value of max_concurrent_runs of HyperDriveConfig to 4.
  • C. Set the value of slack factor of early_termination_policy to 0.1.
  • D. Set the value of max_concurrent_runs of HyperDriveConfig to 20.
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Suggested Answer: B 🗳️
by Tommo565 at March 21, 2023, 2:18 p.m.

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esimsek
Highly Voted 1 year, 2 months ago
Selected Answer: B
The number of concurrent jobs has an impact on the effectiveness of the tuning process. A "smaller number of concurrent jobs" may lead to "better sampling convergence", since the smaller degree of parallelism increases the number of jobs that benefit from previously completed jobs.
upvoted 6 times
snegnik
1 year ago
it's true, because we use Bayesian sampling
upvoted 2 times
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evangelist
Most Recent 4 days, 10 hours ago
B is correct: Bayesian optimization uses the results of previous runs to inform the selection of the next set of hyperparameters. Running too many concurrent runs can reduce the effectiveness of Bayesian optimization because the results of the previous runs are not immediately available to inform the next set of runs.
upvoted 1 times
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deyoz
3 months, 1 week ago
does bayesian sampling has early termination policy? i understand, only random and grid sampling have?
upvoted 1 times
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Mal42
9 months, 3 weeks ago
On exam 18 Aug 2023
upvoted 4 times
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oakmm
1 year, 2 months ago
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters#bayesian-sampling
upvoted 2 times
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Tommo565
1 year, 2 months ago
Answer is correct.
upvoted 1 times
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Tommo565
1 year, 2 months ago
Selected Answer: D
I *think* this is D
upvoted 1 times
Tommo565
1 year, 2 months ago
Please delete this comment.
upvoted 5 times
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