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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

Question 1

5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?

A. A job contains multiple tasks, and each task contains multiple stages.
B. A stage contains multiple tasks, and each task contains multiple jobs.
C. A job contains multiple stages, and each stage contains multiple tasks.
D. A stage contains multiple jobs, and each job contains multiple tasks.

Question 2

22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
B. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
C. spark.read.parquet("/data/parquet/")
D. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")

Question 3

34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A. Reduce the size of the data partitions to improve task scheduling.
B. Increase the executor memory allocation in the Spark configuration.
C. Increase the number of executor instances to handle more concurrent tasks.
D. Set the spark.network.timeout property to allow tasks more time to complete without being killed.

Question 4

11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A. spark.executor.memory
B. spark.sql.shuffle.partitions
C. spark.executor.cores
D. spark.task.maxFailures

Question 5

A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:

import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?

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

Solutions: