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Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Oct 13, 2026
- Q & A: 135 Questions and Answers
About Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5 Exam
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| DataFrame API with PySpark | - DataFrame creation and schema management - Built-in functions and expressions - Transformations and actions |
| Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Structured Streaming Basics | - Windowed aggregations in streaming - Streaming DataFrames |
| Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Data Processing and Performance | - Joins and data partitioning - Optimization techniques - Caching and persistence strategies |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question #1
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:
Which block of Spark code can be used to achieve this requirement?
Options:
- A. filtered_df = users_raw_df.na.drop(how='any')
- B. filtered_df = users_raw_df.na.drop(thresh=0)
- C. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
- D. filtered_df = users_raw_df.na.drop(how='all')
Correct Answer: A 🗳️
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Question #2
33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?
- A. raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
- B. raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))
- C. raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
- D. raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
Correct Answer: C 🗳️
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Question #3
47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?
- A. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
- B. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
- C. Shuffle join because no broadcast hints were provided.
- D. Broadcast join, as df2 is smaller than the default broadcast threshold.
Correct Answer: D 🗳️
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Question #4
9 of 55.
Given the code fragment:
import pyspark.pandas as ps
pdf = ps.DataFrame(data)
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. pdf.to_pandas()
- B. pdf.to_spark()
- C. pdf.spark()
- D. pdf.to_dataframe()
Correct Answer: B 🗳️
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Question #5
A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory.
What is the likely behavior when Spark runs out of memory to store the DataFrame?
- A. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance.
- B. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead.
- C. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely.
- D. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization.
Correct Answer: B 🗳️
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