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Databricks Certified Machine Learning Professional

Last Update Jun 18, 2024
Total Questions : 60

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Last Update Jun 18, 2024
Total Questions : 60

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Databricks-Machine-Learning-Associate Total Questions : 74 Updated : Jun 18, 2024

Databricks Certified Machine Learning Professional Questions and Answers

Questions 1

A data scientist has created a Python functioncompute_featuresthat returns a Spark DataFrame with the following schema:

Databricks-Machine-Learning-Professional Question 1

The resulting DataFrame is assigned to thefeatures_dfvariable. The data scientist wants to create a Feature Store table usingfeatures_df.

Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Clientfs?

Options:

A.

B.

C.

features_df.write.mode("fs").path("new_table")

D.

E.

features_df.write.mode("feature").path("new_table")

Questions 2

A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.

Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

Options:

A.

Batch servinghas built-in capabilities in Databricks Machine Learning

B.

There is no advantage to using batch serving deployments over real-time serving deployments

C.

Computing predictions in real-time provides more up-to-date results

D.

Testing is not possible in real-time serving deployments

E.

Querying stored predictions can be faster than computing predictions in real-time

Questions 3

A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.

Which of the following tools can be used to provide this type of continuous processing?

Options:

A.

Spark UDFs

B.

[Structured Streaming

C.

MLflow

D Delta Lake

D.

AutoML