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Databricks-Machine-Learning-Associate dumps questions answers

Databricks Databricks-Machine-Learning-Associate Dumps

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Databricks-Machine-Learning-Associate
Exam Name:
Databricks Certified Machine Learning Associate Exam
Last Update: Nov 17, 2024
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Databricks Certified Machine Learning Associate Exam Questions and Answers

Questions 1

A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day.

Which approach should the data scientist take to complete this task?

Options:

A.

They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.

B.

They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.

C.

They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes.

D.

They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.

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

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

Options:

A.

pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata

B.

pandas API on Spark DataFrames are more performant than Spark DataFrames

C.

pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

D.

pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

E.

pandas API on Spark DataFrames are unrelated to Spark DataFrames