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

1. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.dropna(how="any")
B) filtered_users = raw_users.na.drop("any")
C) filtered_users = raw_users.na.drop("all")
D) filtered_users = raw_users.dropna(how="all")

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

3. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").parquet("/data/output")
B) df.write.partitionBy("country").parquet("/data/output")
C) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
D) df.write.mode("append").partitionBy("country").parquet("/data/output")

4. A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
B) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
C) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
D) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")

5. 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 stage contains multiple jobs, and each job contains multiple tasks.
D) A job contains multiple stages, and each stage contains multiple tasks.

Solutions: