Databricks Associate-Developer-Apache-Spark-3.5 Q&A - in .pdf

  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Sep 17, 2026
  • Q & A: 135 Questions and Answers
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  • Exam Code: Associate-Developer-Apache-Spark-3.5
  • Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Updated: Sep 17, 2026
  • Q & A: 135 Questions and Answers
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

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

Question #1

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.parquet("/data/parquet/").option("mergeAllCols", True)
  • B. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
  • C. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
  • D. spark.read.parquet("/data/parquet/")
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

Explanation: Only visible for Prep4sureGuide members. You can sign-up / login (it's free).

Question #2

A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

  • A. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort('region_id')
    .take(3)
    )
  • B. regions = dict(
    regions_df
    .select('region_id', 'region')
    .limit(3)
    .collect()
    )
  • C. regions = dict(
    regions_df
    .select('region_id', 'region')
    .sort('region_id')
    .take(3)
    )
  • D. regions = dict(
    regions_df
    .select('region', 'region_id')
    .sort(desc('region_id'))
    .take(3)
    )
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Prep4sureGuide members. You can sign-up / login (it's free).

Question #3

A data engineer wants to process a streaming DataFrame that receives sensor readings every second with columns sensor_id, temperature, and timestamp. The engineer needs to calculate the average temperature for each sensor over the last 5 minutes while the data is streaming.
Which code implementation achieves the requirement?
Options from the images provided:

  • A.
  • B.
  • C.
  • D.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Prep4sureGuide members. You can sign-up / login (it's free).

Question #4

24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

  • A. spark.read.parquet("events.parquet").printSchema()
  • B. spark.sql("SELECT schema FROM events.parquet").show()
  • C. spark.sql("SELECT * FROM events.parquet").show()
  • D. spark.read.format("parquet").load("events.parquet").show()
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Explanation: Only visible for Prep4sureGuide members. You can sign-up / login (it's free).

Question #5

A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

  • A. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
  • B. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
  • C. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
  • D. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for Prep4sureGuide members. You can sign-up / login (it's free).

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