Databricks Associate-Developer-Apache-Spark-3.5 Questions & Answers - in .pdf
- Total Q&A: 135
- Update: Sep 01, 2026
- Price: $59.99
- Vendor: Databricks
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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- Total Q&A: 135
- Update: Sep 01, 2026
- Price: $59.99
- Vendor: Databricks
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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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 Price: | $200 USD |
| Exam Format: | Multiple Choice |
| Exam Duration: | 90 minutes |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| 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 |
|---|---|---|
| Structured Streaming | 10% | - Fault tolerance and state management - Defining streaming queries - Streaming concepts and architecture - Output modes and triggers |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Identifying performance bottlenecks - Debugging and logging - Managing memory and resource usage - Optimizing transformations and actions |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Connecting to remote Spark clusters - Running applications via Spark Connect |
| Using Spark SQL | 20% | - Running SQL queries - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames |
| Apache Spark Architecture and Components | 20% | - Spark architecture overview - Shuffling, actions, and broadcasting - Execution and deployment modes - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection |
| Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Filtering, sorting, and aggregating data - Partitioning and bucketing data - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Creating DataFrames and defining schemas - Reading and writing data in various formats - Handling missing values and data quality |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question 1
A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:
A. CREATE TABLE users USING json OPTIONS (path '/data/input.json')
B. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
D. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
Question 2
12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?
A. filtered_users = raw_users.dropna(how="any")
B. filtered_users = raw_users.dropna(how="all")
C. filtered_users = raw_users.na.drop("all")
D. filtered_users = raw_users.na.drop("any")
Question 3
What is the benefit of Adaptive Query Execution (AQE)?
A. It allows Spark to optimize the query plan before execution but does not adapt during runtime.
B. It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
C. It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D. It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
Question 4
Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?
A. It provides a way to run Spark applications remotely in any programming language
B. It allows for remote execution of Spark jobs
C. It can be used to interact with any remote cluster using the REST API
D. It is primarily used for data ingestion into Spark from external sources
Question 5
A data engineer is working on the DataFrame:
(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?
A. df.select("Name").orderBy(df["Name"].asc())
B. df.select("Name").distinct().orderBy(df["Name"])
C. df.select("Name").distinct()
D. df.select("Name").distinct().orderBy(df["Name"].desc())
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: B | Question 5 Answer: B |
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