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Databricks Associate-Developer-Apache-Spark-3.5 : Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Updated: Sep 22, 2026 Q&As: 135 Questions and Answers
About Databricks Associate-Developer-Apache-Spark-3.5 Exam
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames - Running SQL queries |
| Topic 2: Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Topic 3: Developing Apache Spark DataFrame API Applications | 30% | - Selecting, renaming, and modifying columns - Joining and combining datasets - Filtering, sorting, and aggregating data - Handling missing values and data quality - Partitioning and bucketing data - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Reading and writing data in various formats |
| Topic 4: Structured Streaming | 10% | - Streaming concepts and architecture - Defining streaming queries - Output modes and triggers - Fault tolerance and state management |
| Topic 5: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Topic 6: Apache Spark Architecture and Components | 20% | - Execution and deployment modes - Spark architecture overview - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection |
| Topic 7: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Debugging and logging - Managing memory and resource usage - Identifying performance bottlenecks |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question #1
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?
- A. saveAsTable with mode Overwrite
- B. save with mode Ignore
- C. saveAsTable with mode ErrorIfExists
- D. save with mode ErrorIfExists
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
Question #2
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/")
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
Question #3
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?
- A. Use an RDD action like reduce() to compute the maximum time
- B. Use an accumulator to record the maximum time on the driver
- C. Broadcast a variable to share the maximum time among workers
- D. Configure the Spark UI to automatically collect maximum times
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
Question #4
Given a DataFrame df that has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?
- A. 1
- B. Same number as the cluster executors
- C. 10
- D. 20
Correct Answer: C 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
Question #5
A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?
- A. Switch the deployment mode to cluster mode
- B. Increase the driver memory on the client machine
- C. Add more executor instances to the cluster
- D. Switch the deployment mode to local mode
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTests members. You can sign-up / login (it's free).
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