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

1. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_pandas()
B) psdf.to_dataframe()
C) psdf.to_spark()
D) psdf.to_pyspark()

2. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?

A) It automatically cleans up unused partitions to optimize storage.
B) It optimizes by reading only the relevant subset of data from fewer partitions.
C) It compresses the data to save disk space.
D) It ensures that data is loaded into memory all at once for faster query execution.

3. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Broadcast join, as df2 is smaller than the default broadcast threshold.
B) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
C) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
D) Shuffle join because no broadcast hints were provided.

4. 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 writeStream
C) By configuring the option checkpointLocation during readStream
D) By configuring the option recoveryLocation during the SparkSession initialization

5. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A) Loss of access to past job logs and reduced debugging capability for completed jobs
B) Prevention of driver log accumulation during long-running jobs
C) Enhanced executor performance due to reduced log size
D) Improved job execution speed due to reduced logging overhead

Solutions: