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Exam DP-100 topic 3 question 52 discussion

Actual exam question from Microsoft's DP-100
Question #: 52
Topic #: 3
[All DP-100 Questions]

HOTSPOT -
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:

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Suggested Answer:
Box 1: Yes -
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.

Box 2: Yes -
uniform(low, high) - Returns a value uniformly distributed between low and high

Box 3: No -
Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
by dev2dev at March 20, 2021, 5:02 a.m.

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Anty85
Highly Voted 3 years, 7 months ago
Yes - Bayesian sampling IS based on previous experiments Yes - obviously No - There is no early termination policy in Bayesian sampling
upvoted 40 times
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rishi_ram
Highly Voted 3 years, 5 months ago
Bayesian sampling is based on the Bayesian optimization algorithm. It picks samples based on how previous samples did, so that new samples improve the primary metric. Bayesian sampling only supports choice, uniform, and quniform distributions over the search space. Bayesian sampling does not support early termination. When using Bayesian sampling, set early_termination_policy = None. Based on this Answers are : YES, YES and NO
upvoted 7 times
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NullVoider_0
Most Recent 8 months, 3 weeks ago
On exam 12-02-2024.
upvoted 2 times
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james2033
1 year ago
- Yes - Yes - No Has not any relation between 'Bayesian sampling' to 'Early termination policy for hyperparameter tuning'. Bayesian sampling does not support early termination policies. Reference https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.bayesianparametersampling?view=azure-ml-py#:~:text=Bayesian%20sampling%20does%20not%20support%20early%20termination%20policies.
upvoted 1 times
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rishi_ram
1 year, 5 months ago
https://learn.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.bayesianparametersampling?view=azure-ml-py YES YES NO
upvoted 2 times
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therealola
2 years, 4 months ago
On exam 18-06-22
upvoted 2 times
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racnaoamo
2 years, 5 months ago
similar question on 18-5-22
upvoted 1 times
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tunaktunak
2 years, 11 months ago
On exam 26/11/2021
upvoted 4 times
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JoshuaXu
2 years, 12 months ago
on exam 6 Nov 2021
upvoted 2 times
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ljljljlj
3 years, 3 months ago
On exam 2021/7/10
upvoted 6 times
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ali25
3 years, 6 months ago
yes, yes, no, verify the first
upvoted 3 times
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BilJon
3 years, 7 months ago
Bayesian sampling does not support early termination policies. When using Bayesian parameter sampling, use NoTerminationPolicy, set early termination policy to None, or leave off the early_termination_policy parameter. https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.bayesianparametersampling?view=azure-ml-py
upvoted 1 times
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BilJon
3 years, 7 months ago
Bayesian sampling tries to intelligently pick the next sample of hyperparameters, based on how the previous samples performed, such that the new sample improves the reported primary metric.
upvoted 1 times
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dev2dev
3 years, 7 months ago
1 No -- hyper tuning doesnt consider previous experiment, 2 Yes - because 0.09 falls between given uniform range 3 Yes - You can define early termination
upvoted 1 times
woyaodp100
3 years, 6 months ago
not correct
upvoted 7 times
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Afotechque
3 years, 7 months ago
Bayesian sampling tries to intelligently pick the next sample of hyperparameters, based on how the previous samples performed, such that the new sample improves the reported primary metric
upvoted 2 times
Afotechque
3 years, 7 months ago
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive?view=azure-ml-py
upvoted 1 times
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