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

1. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The same start date will be returned.
B) The number of days specified will be removed from the start date.
C) An error message of an invalid parameter will be returned.
D) The number of days specified will be added to the start date.

2. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) .format("parquet")
.option("location", "path/to/destination/dir")
B) .option("format", "parquet")
.option("location", "path/to/destination/dir")
C) .format("parquet")
.option("path", "path/to/destination/dir")
D) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")

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

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

5. A developer initializes a SparkSession:

spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?

A) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
D) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.

Solutions: