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Databricks Databricks-Machine-Learning-Associate Dumps Questions Answers

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Databricks Certified Machine Learning Associate Exam

Last Update Jun 17, 2024
Total Questions : 74

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All ML Data Scientist Related Certification Exams


Databricks-Machine-Learning-Professional Total Questions : 60 Updated : Jun 17, 2024

Databricks Certified Machine Learning Associate Exam Questions and Answers

Questions 1

A data scientist has defined a Pandas UDF function predict to parallelize the inference process for a single-node model:

They have written the following incomplete code block to use predict to score each record of Spark DataFramespark_df:

Which of the following lines of code can be used to complete the code block to successfully complete the task?

Options:

A.

predict(*spark_df.columns)

B.

mapInPandas(predict)

C.

predict(Iterator(spark_df))

D.

mapInPandas(predict(spark_df.columns))

E.

predict(spark_df.columns)

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 machine learning algorithms typically uses bagging?

Options:

A.

IGradient boosted trees

B.

K-means

C.

Random forest

D.

Decision tree