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

1. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
B) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
C) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
D) A new SparkSession is created every time the getOrCreate() method is invoked.

2. What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A) The conversion will automatically distribute the data across worker nodes
B) The operation will fail if the Pandas DataFrame exceeds 1000 rows
C) The operation will load all data into the driver's memory, potentially causing memory overflow
D) Data will be lost during conversion

3. In the code block below, aggDF contains aggregations on a streaming DataFrame:

Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) replace
B) complete
C) aggregate
D) append

4. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?

A) spark.conf.set("spark.pandas.arrow.enabled", "true")
B) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
C) spark.conf.set("spark.sql.execution.arrow.enabled", "true")
D) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

5. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the executor memory allocation in the Spark configuration.
B) Reduce the size of the data partitions to improve task scheduling.
C) Increase the number of executor instances to handle more concurrent tasks.
D) Set the spark.network.timeout property to allow tasks more time to complete without being killed.

Solutions: