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Apache Spark is a powerful open source data processing engine that provides a unified platform for data analytics, machine learning, and streaming applications. Spark is used to process massive datasets to find patterns and trends in the data, as well as perform data transformations, analyses, and visualizations. The big data industry is growing rapidly, and companies of all sizes are increasingly adopting Spark to analyze their large datasets. In this article, we will discuss about Databricks Associate Developer Apache Spark Exam and how it can help you to become an expert in the world of Big Data.
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Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Cluster Mode and Execution Hierarchy - Partitioning and Fault Tolerance - Driver and Executor Roles |
| Topic 2: Troubleshooting and Tuning | 10% | - Debugging Spark Applications - Caching and Shuffle Optimization - Spark UI and Bottlenecks |
| Topic 3: Using Spark SQL | 20% | - SQL Queries and Operations - Performance Optimization - Window Functions |
| Topic 4: Structured Streaming | 10% | - Watermarks and Triggers - Stateful Processing Basics - Streaming Concepts |
| Topic 5: DataFrame and Dataset API Applications | 30% | - Joins and Transformations - Filtering, Sorting and Aggregation - Column Selection and Manipulation |
| Topic 6: Using Spark Connect and Pandas API on Spark | 10% | - Pandas API Integration - Remote Connectivity |




