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Updated: Oct 03, 2026
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5 |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| Exam Price: | $200 USD |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Recommended Training: | Databricks Academy Training |
| Exam Registration: | Databricks Certification Registration |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored or onsite proctored |
| Pre Condition: | No formal prerequisites; recommended 6+ months hands-on experience with PySpark and DataFrame API |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Key differences and limitations - Converting between Pandas and Spark structures |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Managing memory and resource usage - Debugging and logging - Identifying performance bottlenecks |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Spark architecture overview - Execution and deployment modes - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation |
| Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Running applications via Spark Connect - Spark Connect architecture |
| Structured Streaming | 10% | - Defining streaming queries - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers |
| Using Spark SQL | 20% | - Running SQL queries - Integrating Spark SQL with DataFrames - Working with functions and expressions - Using catalog and metadata APIs |
| Developing Apache Spark DataFrame API Applications | 30% | - Partitioning and bucketing data - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Joining and combining datasets - Filtering, sorting, and aggregating data - Selecting, renaming, and modifying columns - Handling missing values and data quality - Reading and writing data in various formats |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)
- A. Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
- B. Execute their pyspark shell with the option --remote "sc://localhost"
- C. Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
- D. Execute their pyspark shell with the option --remote "https://localhost"
- E. Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
Correct Answer: B,C 🗳️
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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.format("parquet").load("events.parquet").show()
- B. spark.sql("SELECT * FROM events.parquet").show()
- C. spark.read.parquet("events.parquet").printSchema()
- D. spark.sql("SELECT schema FROM events.parquet").show()
Correct Answer: C 🗳️
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How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:
- A. Increase the number of local threads based on the number of CPU cores.
- B. Configure the application to run in cluster mode instead of local mode.
- C. Set the spark.executor.memory property to a large value.
- D. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
Correct Answer: A 🗳️
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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 ErrorIfExists
- B. save with mode Ignore
- C. save with mode ErrorIfExists
- D. saveAsTable with mode Overwrite
Correct Answer: A 🗳️
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A data engineer is streaming data from Kafka and requires:
Minimal latency
Exactly-once processing guarantees
Which trigger mode should be used?
- A. .trigger(continuous='1 second')
- B. .trigger(availableNow=True)
- C. .trigger(processingTime='1 second')
- D. .trigger(continuous=True)
Correct Answer: C 🗳️
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