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所在平台: Udemy |
课程主页: https://www.udemy.com/course/databricks-data-engineer-professional-practice-exam/
课程评论：没有评论
**课程总结：Databricks 数据工程师专业实践 exam** 本课程旨在帮助学员准备 Databricks Certified Data Engineer Professional 认证考试。该认证考试旨在评估个人使用 Databricks 平台执行高级数据工程任务的能力。 **考试重点涵盖：** * **Databricks 工具集 (20%)：** 熟悉 Databricks 平台本身及其开发者工具，包括 Apache Spark、Delta Lake、MLflow、Databricks CLI 和 REST API。 * **数据处理 (30%)：** 重点考察构建高效、优化的 ETL（提取、转换、加载）数据管道的能力。 * **数据建模 (20%)：** 评估利用湖仓一体（Lakehouse）架构进行数据建模的知识，包括通用数据建模概念。 * **安全与治理 (10%)：** 考察确保数据管道安全、可靠的数据治理实践。 * **监控与日志记录 (10%)：** 涵盖如何监控数据管道的运行状态和进行日志分析。 * **测试与部署 (10%)：** 评估在部署前对数据管道进行测试和验证的能力。 **考试形式：** * **类型：** 监考式认证考试 * **总题数：** 60 题 * **时间限制：** 120 分钟 * **题型：** 选择题 * **语言：** 英语 **先修要求与推荐经验：** * **先修要求：** 无。但强烈建议参加相关培训。 * **推荐经验：** 建议具备至少 1 年在考试指南中所列数据工程任务方面的实际操作经验。 **代码语言：** * 考试中的代码示例主要使用 Python。 * Delta Lake 的功能演示将主要以 SQL 语言呈现。 通过本课程的学习和实践，考生将能够掌握使用 Databricks 及其相关工具完成高级数据工程任务的技能，从而顺利通过 Databricks 数据工程师专业认证考试。
The Databricks Certified Data Engineer Professional certification exam assesses an individual's ability to use Databricks to perform advanced data engineering tasks. This includes an understanding of the Databricks platform and developer tools like Apache Spark, Delta Lake, MLflow, and the Databricks CLI and REST API. It also assesses the ability to build optimized and cleaned ETL pipelines. Additionally, the ability to model data into a lakehouse using knowledge of general data modeling concepts will be assessed. Finally, being able to ensure that data pipelines are secure, reliable, monitored and tested before deployment will also be included in this exam. Individuals who pass this certification exam can be expected to complete advanced data engineering tasks using Databricks and its associated tools.The exam covers:Databricks Tooling - 20%Data Processing - 30%Data Modeling - 20%Security and Governance - 10%Monitoring and Logging - 10%Testing and Deployment - 10%Type Proctored certificationTotal number of questions 60Time limit 120 minutes Question types Multiple choice Languages EnglishPrerequisites None, but related training highly recommendedRecommended experience 1 years of hands-on experience performing the data engineering tasks outlined in the exam guideCode examples in this exam will primarily be in Python. However, any and all references to Delta Lake functionality will be made in SQL.
