Databricks Exam Databricks Machine Learning Associate Topic 3 Question 31 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam

Question #: 31
Topic #: 3

[All Databricks Machine Learning Associate Questions]

A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Which of the following changes do they need to make to the above code block in order to accomplish the task?

AChange SparkTrials() to Trials()

BReduce num_evals to be less than 10

CChange fmin() to fmax()

DRemove the trials=trials argument

ERemove the algo=tpe.suggest argument

Show Suggested Answer

Suggested Answer: A

The SparkTrials() is used to distribute trials of hyperparameter tuning across a Spark cluster. If the environment does not support Spark or if the user prefers not to use distributed computing for this purpose, switching to Trials() would be appropriate. Trials() is the standard class for managing search trials in Hyperopt but does not distribute the computation. If the user is encountering issues with SparkTrials() possibly due to an unsupported configuration or an error in the cluster setup, using Trials() can be a suitable change for running the optimization locally or in a non-distributed manner.

Reference

Hyperopt documentation: http://hyperopt.github.io/hyperopt/

by Annelle at Jul 09, 2025, 07:34 PM

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Gail

2 days ago

I'm a bit unsure, but I think reducing num_evals to less than 10 could limit the search space too much. We usually want more evaluations, right?

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Sherly

8 days ago

I remember we discussed the importance of using SparkTrials for distributed hyperparameter tuning, so I think changing it to Trials() might not be correct.

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Jamal

13 days ago

This seems straightforward enough. I'll carefully analyze the code and the provided options to determine the most effective change to make in order to accomplish the task.

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Alease

19 days ago

I've worked with similar hyperparameter tuning problems before, so I think I have a good handle on this. My initial instinct is that option A, changing SparkTrials() to Trials(), might be the right approach.

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Monroe

24 days ago

I'm a bit confused by the options here. I'll need to review the documentation on the Hyperopt library and Spark ML to determine the most appropriate changes to make.

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Reita

29 days ago

Okay, let me think this through step-by-step. The key seems to be understanding the differences between the Trials and SparkTrials classes and how they impact the hyperparameter tuning process.

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Carin

1 month ago

Hmm, this looks like a tricky one. I'll need to carefully read through the code and understand the context to figure out the best approach.

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Felton

3 months ago

This is a tricky one, but I reckon the answer is E. Removing the algo=tpe.suggest argument could help the model tune the hyperparameters better. Besides, who needs algorithms when you've got pure, unadulterated luck? *winks*

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Jerrod

3 months ago

Hmm, I'm not so sure. Changing fmin() to fmax() seems a bit odd. I'm leaning towards D - removing the trials=trials argument might be the way to go.

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Beatriz

3 months ago

No way, B is wrong! We need to keep a decent number of evaluations to find the best hyperparameters. I'd go with A - changing SparkTrials() to Trials() seems like the right move here.

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