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

1. 19 of 55.
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function not available in the standard Spark functions library.
The existing UDF code is:
import hashlib
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = udf(shake_256, StringType())
The developer replaces this UDF with a Pandas UDF for better performance:
@pandas_udf(StringType())
def shake_256(raw: str) -> str:
return hashlib.shake_256(raw.encode()).hexdigest(20)
However, the developer receives this error:
TypeError: Unsupported signature: (raw: str) -> str
What should the signature of the shake_256() function be changed to in order to fix this error?

A) def shake_256(raw: [pd.Series]) -> pd.Series:
B) def shake_256(raw: [str]) -> [str]:
C) def shake_256(raw: str) -> str:
D) def shake_256(raw: pd.Series) -> pd.Series:

2. A data engineer is running a batch processing job on a Spark cluster with the following configuration:
10 worker nodes
16 CPU cores per worker node
64 GB RAM per node
The data engineer wants to allocate four executors per node, each executor using four cores.
What is the total number of CPU cores used by the application?

A) 160
B) 80
C) 40
D) 64

3. 7 of 55.
A developer has been asked to debug an issue with a Spark application. The developer identified that the data being loaded from a CSV file is being read incorrectly into a DataFrame.
The CSV file has been read using the following Spark SQL statement:
CREATE TABLE locations
USING csv
OPTIONS (path '/data/locations.csv')
The first lines of the command SELECT * FROM locations look like this:
| city | lat | long |
| ALTI Sydney | -33... | ... |
Which parameter can the developer add to the OPTIONS clause in the CREATE TABLE statement to read the CSV data correctly again?

A) 'header' 'false'
B) 'sep' ','
C) 'header' 'true'
D) 'sep' '|'

4. Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:

A) spark.task.maxFailures
B) spark.executor.memory
C) spark.executor.cores
D) spark.driver.cores

5. A developer is running Spark SQL queries and notices underutilization of resources. Executors are idle, and the number of tasks per stage is low.
What should the developer do to improve cluster utilization?

A) Increase the size of the dataset to create more partitions
B) Enable dynamic resource allocation to scale resources as needed
C) Increase the value of spark.sql.shuffle.partitions
D) Reduce the value of spark.sql.shuffle.partitions

Solutions: