Try and practice the latest Databricks : Associate-Developer-Apache-Spark real questions & answers
Updated: Sep 13, 2026
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Prerequisites for the Databricks Associate Developer Apache Spark Exam?
- You need to be able to complete the individual data manipulation task with the help of the Spark DataFrameAPI.
- If you have a basic understanding of the architecture, you can use adaptive query execution.
Exam Format and Content
Language: This exam is only available in the Python or Scala language.
Exam Length: 60 questions
Exam Duration: 120 minutes
Passing score: 70%
Exam Format: Multiple choice questions
Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.0 Exam |
| Exam Number: | Associate-Developer-Apache-Spark |
| Passing Score: | 70% |
| Available Languages: | English |
| Real Exam Qty: | 60 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Related Certifications: | Databricks Certified Associate Data Engineer Databricks Certified Professional Data Engineer |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Price: | $200 USD |
| Recommended Training: | Databricks Academy - Apache Spark for Developers Databricks Exam Guide |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark Sample Questions |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
| Topic 2: Developing DataFrame API Applications | 44% | - Handling missing data and duplicates - Reading/writing data and partitioning - Column and row manipulation - Filtering, sorting, aggregation - User-defined functions (UDFs) |
| Topic 3: Using Spark SQL | 12% | - Querying data with Spark SQL - Supported file formats and data sources - Working with views and tables |
| Topic 4: Apache Spark Architecture and Components | 28% | - Lazy evaluation, transformations vs actions - Spark execution model and hierarchy - Driver and executor architecture - Fault tolerance and shuffle operations |
| Topic 5: Structured Streaming | 8% | - Streaming concepts and operations - Output modes and triggers |
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