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Last Updated: Sep 13, 2026
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing Apache Spark DataFrame API Applications | 30% | - Handling missing values and data quality - Selecting, renaming, and modifying columns - Creating DataFrames and defining schemas - Partitioning and bucketing data - Reading and writing data in various formats - Filtering, sorting, and aggregating data - User-defined functions (UDFs) - Joining and combining datasets |
| Topic 2: Apache Spark Architecture and Components | 20% | - Shuffling, actions, and broadcasting - Fault tolerance and garbage collection - Execution hierarchy and lazy evaluation - Execution and deployment modes - Spark architecture overview |
| Topic 3: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Debugging and logging - Managing memory and resource usage - Identifying performance bottlenecks |
| Topic 4: Using Spark SQL | 20% | - Running SQL queries - Working with functions and expressions - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs |
| Topic 5: Structured Streaming | 10% | - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers - Defining streaming queries |
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Topic 7: Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A stage contains multiple jobs, and each job contains multiple tasks.
- B. A job contains multiple tasks, and each task contains multiple stages.
- C. A stage contains multiple tasks, and each task contains multiple jobs.
- D. A job contains multiple stages, and each stage contains multiple tasks.
Correct Answer: D 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:
- A. Save the users_vw definition and query using Spark
- B. Recreate the users_vw and query the data using Spark
- C. Query the users_vw using Spark
- D. Persist the users_vw data as a table
Correct Answer: D 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
17 of 55.
A data engineer has noticed that upgrading the Spark version in their applications from Spark 3.0 to Spark 3.5 has improved the runtime of some scheduled Spark applications.
Looking further, the data engineer realizes that Adaptive Query Execution (AQE) is now enabled.
Which operation should AQE be implementing to automatically improve the Spark application performance?
- A. Improving the performance of single-stage Spark jobs
- B. Collecting persistent table statistics and storing them in the metastore for future use
- C. Dynamically switching join strategies
- D. Optimizing the layout of Delta files on disk
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?
- A. df = au_df.unionByName(nz_df, allowMissingColumns=False)
- B. df = au_df.unionAll(nz_df)
- C. df = au_df.unionByName(nz_df, allowMissingColumns=True)
- D. df = au_df.union(nz_df, allowMissingColumns=True)
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?
- A. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
- B. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- C. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
- D. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
Correct Answer: C 🗳️
Explanation: Only visible for Test4Sure members. You can sign-up / login (it's free).
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