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所在平台: Udemy |
课程主页: https://www.udemy.com/course/databricks-certified-professional-data-engineer/
课程评论：没有评论
课程名称：Databricks认证专业数据工程师考试 课程概述：如果您正在寻找Databricks数据工程师专业认证考试的练习测试，您来对地方了！本课程提供了四个练习测试，并附有详细解释，以帮助您为实际考试做好准备。这些练习测试涵盖以下考试主题，并提供详尽的解释： - **数据管理解决方案模型**，包括： - Lakehouse（青铜/白银/黄金架构、表、视图和物理布局） - 一般数据建模概念（约束、查找表、缓慢变化的维度） - **使用Spark和Delta Lake API构建数据处理管道**，包括： - 构建批处理ETL管道 - 构建增量处理ETL管道 - 数据去重 - 使用变更数据捕获（CDC）传播变更 - 优化工作负载 - **了解如何使用Databricks平台及其工具的好处**，包括： - Databricks CLI（部署基于笔记本的工作流） - Databricks REST API（配置和触发生产管道） - **根据安全性和治理最佳实践构建生产管道**，包括： - 使用ACL管理集群和作业权限 - 创建行和列导向的动态视图以控制用户/组访问 - 根据GDPR和CCPA的要求安全删除数据 - **配置警报和存储以监控和记录生产作业**，包括： - 记录日志度量 - 调试错误 - **遵循管理、测试和部署代码的最佳实践**，包括： - 相对导入 - 调度作业 - orchestration作业 本课程旨在通过系列练习测试和详细说明，帮助学员为Databricks数据工程师专业认证考试做好全面准备。
If you looking for practice tests for Databricks Data Engineer Professional certification exam, you have come to the right place! Four practice tests with detailed explanations are available to prepare you before appearing for the actual exam.The practice tests cover the following exam topics with detailed explanations:Model data management solutions, including:Lakehouse (bronze/silver/gold architecture, tables, views, and the physical layout)General data modeling concepts (constraints, lookup tables, slowly changing dimensions)Build data processing pipelines using the Spark and Delta Lake APIs, including:Building batch-processed ETL pipelinesBuilding incrementally processed ETL pipelinesDeduplicating dataUsing Change Data Capture (CDC) to propagate changesOptimizing workloadsUnderstand how to use and the benefits of using the Databricks platform and its tools, including:Databricks CLI (deploying notebook-based workflows)Databricks REST API (configure and trigger production pipelines)Build production pipelines using best practices around security and governance, including:Managing clusters and jobs permissions with ACLsCreating row- and column-oriented dynamic views to control user/group accessSecurely delete data as requested according to GDPR & CCPAConfigure alerting and storage to monitor and log production jobs, including:Recording logged metricsDebugging errorsFollow best practices for managing, testing and deploying code, including:Relative importsScheduling JobsOrchestration Jobs
