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Updated: Aug 05, 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 |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45 |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam via Kryterion Webassessor or authorized test center. |
| Pre Condition: | No formal prerequisites. Databricks recommends at least 6 months of hands-on Apache Spark experience and Python programming knowledge. |
| 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 |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 2: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 3: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 4: Structured Streaming | 10% | - Streaming Applications
|
| Topic 5: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 6: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 7: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:
A) Use the repartition() transformation with a lower number of partitions
B) Use the coalesce() transformation with a lower number of partitions
C) Use the distinct() transformation to combine similar partitions
D) Use the sortBy() transformation to reorganize the data
2. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?
A) A Cartesian join
B) A shuffled hash join
C) A sort-merge join
D) A broadcast nested loop join
3. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?
A) The number of days specified will be removed from the start date.
B) The number of days specified will be added to the start date.
C) An error message of an invalid parameter will be returned.
D) The same start date will be returned.
4. A developer initializes a SparkSession:
spark = SparkSession.builder \
.appName("Analytics Application") \
.getOrCreate()
Which statement describes the spark SparkSession?
A) The getOrCreate() method explicitly destroys any existing SparkSession and creates a new one.
B) A new SparkSession is created every time the getOrCreate() method is invoked.
C) A SparkSession is unique for each appName, and calling getOrCreate() with the same name will return an existing SparkSession once it has been created.
D) If a SparkSession already exists, this code will return the existing session instead of creating a new one.
5. A Spark application developer wants to identify which operations cause shuffling, leading to a new stage in the Spark execution plan.
Which operation results in a shuffle and a new stage?
A) DataFrame.select()
B) DataFrame.withColumn()
C) DataFrame.groupBy().agg()
D) DataFrame.filter()
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: C |
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