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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 data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A. 80
B. 160
C. 40
D. 64

Question 2

How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A. Set the spark.executor.memory property to a large value.
B. Configure the application to run in cluster mode instead of local mode.
C. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
D. Increase the number of local threads based on the number of CPU cores.

Question 3

A Data Analyst is working on the DataFrame sensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits the record column into separate columns and has one array item per row?
A)

B)

C)

D)

A. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
B. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
C. exploded_df = exploded_df.select("record_datetime", "record_exploded")
D. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")

Question 4

11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A. spark.sql.shuffle.partitions
B. spark.executor.cores
C. spark.task.maxFailures
D. spark.executor.memory

Question 5

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

Solutions: