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Databricks Certification Databricks-Certified-Professional-Data-Engineer Dumps PDF

Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 29

When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

Options:

A.

The five Minute Load Average remains consistent/flat

B.

Bytes Received never exceeds 80 million bytes per second

C.

Network I/O never spikes

D.

Total Disk Space remains constant

E.

CPU Utilization is around 75%

Question 30

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.

Question 31

What is the first of a Databricks Python notebook when viewed in a text editor?

Options:

A.

%python

B.

% Databricks notebook source

C.

-- Databricks notebook source

D.

//Databricks notebook source

Question 32

A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.

Which of the following likely explains these smaller file sizes?

Options:

A.

Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

B.

Z-order indices calculated on the table are preventing file compaction

C Bloom filler indices calculated on the table are preventing file compaction

C.

Databricks has autotuned to a smaller target file size based on the overall size of data in the table

D.

Databricks has autotuned to a smaller target file size based on the amount of data in each partition