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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:

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

2. An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?

A) spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
B) spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
C) spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
D) spark.read.orc("/file/test_data.orc").selected("col1", "col2")

3. 31 of 55.
Given a DataFrame df that has 10 partitions, after running the code:
df.repartition(20)
How many partitions will the result DataFrame have?

A) 20
B) 10
C) Same number as the cluster executors
D) 5

4. A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns for transaction_id, account_number, transaction_amount, and timestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

A) df = df.dropDuplicates(["transaction_amount"])
B) df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first("timestamp"))
C) df = df.dropDuplicates()
D) df = df.filter(F.col("transaction_id").isNotNull())

5. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

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

Solutions: