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Databricks Updated Databricks-Certified-Professional-Data-Engineer Exam Questions and Answers by mae

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview :

Exam Name: Databricks Certified Data Engineer Professional Exam
Exam Code: Databricks-Certified-Professional-Data-Engineer Dumps
Vendor: Databricks Certification: Databricks Certification
Questions: 120 Q&A's Shared By: mae
Question 20

A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.

Which kind of the test does the above line exemplify?

Options:

A.

Integration

B.

Unit

C.

Manual

D.

functional

Discussion
Question 21

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

Options:

A.

Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

B.

Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

C.

Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

D.

By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

Discussion
Question 22

What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

Options:

A.

Use &Pip install in a notebook cell

B.

Run source env/bin/activate in a notebook setup script

C.

Install libraries from PyPi using the cluster UI

D.

Use &sh install in a notebook cell

Discussion
Question 23

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

Options:

A.

spark.sql.files.maxPartitionBytes

B.

spark.sql.autoBroadcastJoinThreshold

C.

spark.sql.files.openCostInBytes

D.

spark.sql.adaptive.coalescePartitions.minPartitionNum

E.

spark.sql.adaptive.advisoryPartitionSizeInBytes

Discussion
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