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Updated: Aug 19, 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 |
| Passing Score: | 70% |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45 |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple Choice |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| 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 Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Working with functions and expressions - Running SQL queries |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Debugging and logging - Optimizing transformations and actions - Identifying performance bottlenecks |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Connecting to remote Spark clusters - Spark Connect architecture |
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Developing Apache Spark DataFrame API Applications | 30% | - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Selecting, renaming, and modifying columns - Reading and writing data in various formats - Partitioning and bucketing data - Joining and combining datasets - Handling missing values and data quality |
| Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Execution and deployment modes - Spark architecture overview - Fault tolerance and garbage collection |
| Structured Streaming | 10% | - Output modes and triggers - Streaming concepts and architecture - Fault tolerance and state management - Defining streaming queries |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?
A) ALL, DEBUG, FAIL, INFO
B) WARN, NONE, ERROR, FATAL
C) FATAL, NONE, INFO, DEBUG
D) ERROR, WARN, TRACE, OFF
2. Given the code:
df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?
A) When the count action is applied
B) When the groupBy transformation is applied
C) When the show action is applied
D) When the filter transformation is applied
3. 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) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
B) Execute their pyspark shell with the option --remote "sc://localhost"
C) Execute their pyspark shell with the option --remote "https://localhost"
D) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
E) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
4. 5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
A) A stage contains multiple tasks, and each task contains multiple jobs.
B) A job contains multiple tasks, and each task contains multiple stages.
C) A job contains multiple stages, and each stage contains multiple tasks.
D) A stage contains multiple jobs, and each job contains multiple tasks.
5. A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)
B)
C)
D)
Options:
A) Uses trigger() - default micro-batch trigger without interval.
B) Uses trigger(processingTime='5 seconds') - correct micro-batch trigger with interval.
C) Uses trigger(continuous='5 seconds') - continuous processing mode.
D) Uses trigger(processingTime=5000) - invalid, as processingTime expects a string.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B,E | Question # 4 Answer: C | Question # 5 Answer: B |
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