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

1. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
B) It filters the id values before performing the join.
C) It ensures that the join happens only when the id values are identical.
D) It increases the partition size for df1 and df2.

2. A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

A) Transformations are executed immediately to build the lineage graph.
B) The Spark engine optimizes the execution plan during the transformations, causing delays.
C) The Spark engine requires manual intervention to start executing transformations.
D) Transformations are evaluated lazily.
E) Only actions trigger the execution of the transformation pipeline.

3. A developer initializes a SparkSession:

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

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

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

A) Increase the number of local threads based on the number of CPU cores.
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) Set the spark.executor.memory property to a large value.

5. A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*") \
.select('*', '_metadata.file_path')
Which change will ensure one record per file?
Options:

A) Add the option wholetext=False to the text() function
B) Add the option wholetext=True to the text() function
C) Add the option lineSep=", " to the text() function
D) Add the option lineSep='\n' to the text() function

Solutions: