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

1. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A) By configuring the option checkpointLocation during writeStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option recoveryLocation during writeStream
D) By configuring the option checkpointLocation during readStream

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

3. Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:

A) Persist the users_vw data as a table
B) Save the users_vw definition and query using Spark
C) Query the users_vw using Spark
D) Recreate the users_vw and query the data using Spark

4. 4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

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

5. 28 of 55.
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) coalesce
B) groupBy
C) select
D) filter

Solutions: