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

1. Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

A) 10
B) Same number as the cluster executors
C) 20
D) 1

2. A developer initializes a SparkSession:

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

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

3. A data engineer needs to persist a file-based data source to a specific location. However, by default, Spark writes to the warehouse directory (e.g., /user/hive/warehouse). To override this, the engineer must explicitly define the file path.
Which line of code ensures the data is saved to a specific location?
Options:

A) users.write(path="/some/path").saveAsTable("default_table")
B) users.write.option("path", "/some/path").saveAsTable("default_table")
C) users.write.saveAsTable("default_table", path="/some/path")
D) users.write.saveAsTable("default_table").option("path", "/some/path")

4. 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) select
B) coalesce
C) filter
D) groupBy

5. A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?

A) Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
B) Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
C) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
D) Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.

Solutions: