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

1. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='alladin', age=20)]
B) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
C) [Row(name='bambi'), Row(name='alladin', age=20)]
D) The code throws an error due to a schema mismatch.

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

3. 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:

4. 28 of 55.
A data analyst builds a Spark application to analyze finance data and performs the following operations:
filter, select, groupBy, and coalesce.
Which operation results in a shuffle?

A) select
B) groupBy
C) filter
D) coalesce

5. 35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?

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

Solutions: