Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Exam Dumps

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Databricks DATABRICKS-MACHINE-LEARNING-ASSOCIATE Exam Dumps

Vendor: Databricks
Exam Code: DATABRICKS-MACHINE-LEARNING-ASSOCIATE
Exam Name: Databricks Certified Machine Learning Associate Exam
Certification: ML Data Scientist
Total Questions: 74 Q&A
Updated on: Dec 15, 2024
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DATABRICKS-MACHINE-LEARNING-ASSOCIATE Online Practice Questions and Answers

Questions 1

A data scientist wants to use Spark ML to one-hot encode the categorical features in their PySpark DataFramefeatures_df. A list of the names of the string columns is assigned to theinput_columnsvariable.

They have developed this code block to accomplish this task:

The code block is returning an error.

Which of the following adjustments does the data scientist need to make to accomplish this task?

A. They need to specify the method parameter to the OneHotEncoder.

B. They need to remove the line with the fit operation.

C. They need to use Stringlndexer prior to one-hot encodinq the features.

D. They need to useVectorAssemblerprior to one-hot encoding the features.

Show Answer

Questions 2

A machine learning engineer is trying to perform batch model inference. They want to get predictions using the linear regression model saved at the pathmodel_urifor the DataFramebatch_df.

batch_dfhas the following schema:

customer_id STRING

The machine learning engineer runs the following code block to perform inference onbatch_dfusing the linear regression model atmodel_uri:

In which situation will the machine learning engineer's code block perform the desired inference?

A. When the Feature Store feature set was logged with the model at model_uri

B. When all of the features used by the model at model_uri are in a Spark DataFrame in the PySpark

C. When the model at model_uri only uses customer_id as a feature

D. This code block will not perform the desired inference in any situation.

E. When all of the features used by the model at model_uri are in a single Feature Store table

Show Answer

Questions 3

A machine learning engineer is trying to scale a machine learning pipeline by distributing its feature engineering process.

Which of the following feature engineering tasks will be the least efficient to distribute?

A. One-hot encoding categorical features

B. Target encoding categorical features

C. Imputing missing feature values with the mean

D. Imputing missing feature values with the true median

E. Creating binary indicator features for missing values

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