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

1. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
D) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).

2. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) In-memory computation and parallel processing capabilities
B) Built-in machine learning libraries
C) Ability to process small datasets efficiently
D) Support for SQL queries on structured data

3. 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 stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
D) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.

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

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

Solutions: