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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

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.

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Question 2

A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.

When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Total Disk Space remains constant

D.

Network I/O never spikes

E.

Overall cluster CPU utilization is around 25%

Question 3

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.

Which code snippet completes this function definition?

def new_records():

Options:

A.

return spark.readStream.table("bronze")

B.

return spark.readStream.load("bronze")

C.

D.

return spark.read.option("readChangeFeed", "true").table ("bronze")

E.