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Databricks Certified Data Engineer Professional Exam

Last Update Oct 13, 2024
Total Questions : 120 With Comprehensive Analysis

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Last Update Oct 13, 2024
Total Questions : 120 With Comprehensive Analysis

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Questions 1

The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:

item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING

The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.

A junior data engineer suggests converting this data to Delta Lake will improve query performance.

Which response to the junior data engineer s suggestion is correct?

Options:

A.

Delta Lake statistics are not optimized for free text fields with high cardinality.

B.

Text data cannot be stored with Delta Lake.

C.

ZORDER ON review will need to be run to see performance gains.

D.

The Delta log creates a term matrix for free text fields to support selective filtering.

E.

Delta Lake statistics are only collected on the first 4 columns in a table.

Questions 2

A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.

Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

Options:

A.

Use Repos to merge all differences and make a pull request back to the remote repository.

B.

Use repos to merge all difference and make a pull request back to the remote repository.

C.

Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.

D.

Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

Questions 3

The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.

The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

Options:

A.

Model, predict (df, columns)

B.

Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

C.

Df. Select (''customer_id''.

Model (''columns) alias (''predictions'')

D.

Df.apply(model, columns). Select (''customer_id, prediction''

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