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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

Question 1

A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A. Enable dynamic resource allocation to scale resources as needed
B. Increase the value of spark.sql.shuffle.partitions
C. Reduce the value of spark.sql.shuffle.partitions
D. Increase the size of the dataset to create more partitions

Question 2

16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A. Transformations are executed immediately to build the lineage graph.
B. Transformations are evaluated lazily.
C. Only actions trigger the execution of the transformation pipeline.
D. The Spark engine requires manual intervention to start executing transformations.
E. The Spark engine optimizes the execution plan during the transformations, causing delays.

Question 3

A data analyst builds a Spark application to analyze finance data and performs the following operations: filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A. filter
B. select
C. coalesce
D. groupBy

Question 4

What is a feature of Spark Connect?

A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
C. It supports only PySpark applications
D. It has built-in authentication

Question 5

Given the following code snippet in my_spark_app.py:

What is the role of the driver node?

A. The driver node stores the final result after computations are completed by worker nodes
B. The driver node only provides the user interface for monitoring the application
C. The driver node holds the DataFrame data and performs all computations locally
D. The driver node orchestrates the execution by transforming actions into tasks and distributing them to worker nodes

Solutions: