Databricks Associate-Developer-Apache-Spark-3.5

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

1. A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with acollect()action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?

A) The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
B) Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
C) Thecollect()action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
D) The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.

2. A developer runs:

What is the result?
Options:

A) It creates separate directories for each unique combination of color and fruit.
B) It appends new partitions to an existing Parquet file.
C) It stores all data in a single Parquet file.
D) It throws an error if there are null values in either partition column.

3. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/")
C) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
D) df = spark.read.parquet("/path/events/data/*")

4. A Spark DataFramedfis cached using theMEMORY_AND_DISKstorage 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 stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
B) Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
C) 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.
D) 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.

5. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
B) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B

Solutions: