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Databricks Databricks-Machine-Learning-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Machine-Learning-Associate
Exam Name:
Databricks Certified Machine Learning Associate Exam
Certification:
Vendor:
Questions:
74
Last Updated:
Apr 26, 2025
Exam Status:
Stable
Databricks Databricks-Machine-Learning-Associate

Databricks-Machine-Learning-Associate: ML Data Scientist Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Machine Learning Associate Exam Questions and Answers

Question 1

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column price is greater than 0.

Which of the following code blocks will accomplish this task?

Options:

A.

spark_df[spark_df["price"] > 0]

B.

spark_df.filter(col("price") > 0)

C.

SELECT * FROM spark_df WHERE price > 0

D.

spark_df.loc[spark_df["price"] > 0,:]

E.

spark_df.loc[:,spark_df["price"] > 0]

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Question 2

An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.

Which of the following explanations justifies this suggestion?

Options:

A.

One-hot encoding is not supported by most machine learning libraries.

B.

One-hot encoding is dependent on the target variable's values which differ for each application.

C.

One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.

D.

One-hot encoding is not a common strategy for representing categorical feature variables numerically.

E.

One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

Question 3

A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.

Which of the following lines of code can the data scientist run to accomplish the task?

Options:

A.

spark_df.describe()

B.

dbutils.data(spark_df).summarize()

C.

This task cannot be accomplished in a single line of code.

D.

spark_df.summary()

E.

dbutils.data.summarize (spark_df)