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

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.

Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

Options:

A.

Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

B.

Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

C.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

D.

Both commands will fail. No new variables, tables, or views will be created.

E.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

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Question 2

What statement is true regarding the retention of job run history?

Options:

A.

It is retained until you export or delete job run logs

B.

It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

C.

t is retained for 60 days, during which you can export notebook run results to HTML

D.

It is retained for 60 days, after which logs are archived

E.

It is retained for 90 days or until the run-id is re-used through custom run configuration

Question 3

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.

Which statement is a possible explanation for this behavior?

Options:

A.

%sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

B.

Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

C.

%sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

D.

Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

E.

%sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.