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Databricks Certified Machine Learning Associate Exam

Last Update Jun 11, 2024
Total Questions : 74

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Databricks-Machine-Learning-Professional Total Questions : 60 Updated : Jun 11, 2024

Databricks Certified Machine Learning Associate Exam Questions and Answers

Questions 1

Which of the following hyperparameter optimization methods automatically makes informed selections of hyperparameter values based on previous trials for each iterative model evaluation?

Options:

A.

Random Search

B.

Halving Random Search

C.

Tree of Parzen Estimators

D.

Grid Search

Questions 2

A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.

Which change could the data scientist make to improve their model accuracy over the course of their tuning process?

Options:

A.

Change the number of compute nodes to be half or less than half of the number of evaluations.

B.

Change the number of compute nodes and the number of evaluations to be much larger but equal.

C.

Change the iterative optimization algorithm used to facilitate the tuning process.

D.

Change the number of compute nodes to be double or more than double the number of evaluations.

Questions 3

A machine learning engineer wants to parallelize the training of group-specific models using the Pandas Function API. They have developed thetrain_modelfunction, and they want to apply it to each group of DataFramedf.

They have written the following incomplete code block:

Databricks-Machine-Learning-Associate Question 3

Which of the following pieces of code can be used to fill in the above blank to complete the task?

Options:

A.

applyInPandas

B.

mapInPandas

C.

predict

D.

train_model

E.

groupedApplyIn