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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-Python |
| Real Exam Qty: | 45-60 |
| Exam Format: | Multiple choice, Multiple select |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Passing Score: | 70% |
| Exam Duration: | 90 minutes |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Data Engineer Professional |
| Exam Price: | $200 USD |
| Recommended Training: | Databricks Academy - Apache Spark Training Apache Spark Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Spark SQL | - SQL queries on DataFrames and tables - Window functions and aggregations |
| Topic 2: Structured Streaming Basics | - Streaming DataFrames - Windowed aggregations in streaming |
| Topic 3: Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Topic 4: DataFrame API with PySpark | - DataFrame creation and schema management - Transformations and actions - Built-in functions and expressions |
| Topic 5: Data Processing and Performance | - Optimization techniques - Caching and persistence strategies - Joins and data partitioning |
| Topic 6: Data Ingestion and Storage | - Reading and writing data (Parquet, JSON, CSV) - Delta Lake basics |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
A) By configuring the option recoveryLocation during writeStream
B) By configuring the option checkpointLocation during readStream
C) By configuring the option recoveryLocation during the SparkSession initialization
D) By configuring the option checkpointLocation during writeStream
2. Given the schema:
event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:
A) dropDuplicates on all columns (wrong criteria)
B) groupBy without aggregation (invalid use)
C) dropDuplicates on the exact matching fields
D) dropDuplicates with no arguments (removes based on all columns)
3. 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(processingTime='1 second')
C) .trigger(continuous=True)
D) .trigger(availableNow=True)
4. Which configuration can be enabled to optimize the conversion between Pandas and PySpark DataFrames using Apache Arrow?
A) spark.conf.set("spark.sql.execution.arrow.enabled", "true")
B) spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
C) spark.conf.set("spark.pandas.arrow.enabled", "true")
D) spark.conf.set("spark.sql.arrow.pandas.enabled", "true")
5. 36 of 55.
What is the main advantage of partitioning the data when persisting tables?
A) It ensures that data is loaded into memory all at once for faster query execution.
B) It compresses the data to save disk space.
C) It automatically cleans up unused partitions to optimize storage.
D) It optimizes by reading only the relevant subset of data from fewer partitions.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: D |
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