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Passing score: 70%
Language: This exam is only available in the Python or Scala language.
Exam Length: 60 questions
Exam Format: Multiple choice questions
Exam Duration: 120 minutes
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The Databricks Associate Developer Apache Spark Exam is a certification that can be earned by anyone who has successfully completed the Databricks Associate Developer Apache Spark Certification Training. The exam covers all the material that was covered in the training. The exam is designed to test your knowledge of the concepts, skills, and abilities that you learned during the course.
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The Databricks Associate Developer Apache Spark Exam is a test that aims to assess whether you have the knowledge required to become a certified Apache Spark developer. The Databricks Associate Developer Apache Spark Exam consists of two parts: the first part tests your knowledge of the fundamentals of the Apache Spark framework and the second part tests your ability to apply this knowledge. This post will help you get a head start in preparing for the Databricks Associate Developer Apache Spark Exam. The executors disk division actions documentation frame for the executor syntax variables object return allowed partition for the fit output transformation to induce couple of manager and evaluated expected safely, lazily named nodes broadcast operations for correctly mock driver.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
| Topic 2: Structured Streaming | 8% | - Output modes and triggers - Streaming concepts and operations |
| Topic 3: Apache Spark Architecture and Components | 28% | - Spark execution model and hierarchy - Lazy evaluation, transformations vs actions - Fault tolerance and shuffle operations - Driver and executor architecture |
| Topic 4: Developing DataFrame API Applications | 44% | - Handling missing data and duplicates - User-defined functions (UDFs) - Column and row manipulation - Reading/writing data and partitioning - Filtering, sorting, aggregation |
| Topic 5: Using Spark SQL | 12% | - Supported file formats and data sources - Querying data with Spark SQL - Working with views and tables |
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