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Databricks Certified Associate Developer for Apache Spark 3.5 - Python: Associate-Developer-Apache-Spark-3.5 Exam

"Databricks Certified Associate Developer for Apache Spark 3.5 - Python", also known as Associate-Developer-Apache-Spark-3.5 exam, is a Databricks Certification. With the complete collection of questions and answers, Pass4sureCert has assembled to take you through 135 Q&As to your Associate-Developer-Apache-Spark-3.5 Exam preparation. In the Associate-Developer-Apache-Spark-3.5 exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

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  • Updated on: Sep 09, 2026
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  • Updated on: Sep 09, 2026
  • No. of Questions: 135 Questions & Answers

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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

SectionObjectives
Structured Streaming Basics- Streaming DataFrames
- Windowed aggregations in streaming
Data Processing and Performance- Optimization techniques
- Joins and data partitioning
- Caching and persistence strategies
Apache Spark Fundamentals- Spark architecture and execution model
- RDD vs DataFrame vs Dataset concepts
Spark SQL- SQL queries on DataFrames and tables
- Window functions and aggregations
DataFrame API with PySpark- Transformations and actions
- DataFrame creation and schema management
- Built-in functions and expressions
Data Ingestion and Storage- Delta Lake basics
- Reading and writing data (Parquet, JSON, CSV)

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

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

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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. coalesce
  • D. filter
Answer: B

Explanation: Only visible for Pass4sureCert members. You can sign-up / login (it's free).

You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

  • A. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
  • B. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
  • C. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
  • D. DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
Answer: C

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22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

  • A. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
  • B. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
  • C. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
  • D. spark.read.parquet("/data/parquet/")
Answer: C

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37 of 55.
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 = customerDF \
    .withColumn("username", split(col("email"), "@").getItem(0)) \
    .withColumn("domain", split(col("email"), "@").getItem(1))
  • B. customerDF = customerDF.select("email").alias("username", "domain")
  • C. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
  • D. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
Answer: A

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