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

1. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
C) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))
D) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)

2. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

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

3. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) saveAsTable with mode Overwrite
B) save with mode ErrorIfExists
C) saveAsTable with mode ErrorIfExists
D) save with mode Ignore

4. What is the behavior for function date_sub(start, days) if a negative value is passed into the days parameter?

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

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

Solutions: