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所在平台: Udemy |
课程主页: https://www.udemy.com/course/cdmp-data-management-exam-prep-800-practice-questions/
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Coursera 课程总结：《CDMP 数据管理考试准备：800 道练习题》 本课程提供 800 道练习题，旨在帮助您熟练掌握 CDMP 数据管理考试的知识点，并熟悉考试题型。课程包含 6 套练习测试： * **3 套模拟考试：** 每道题限时 1 分钟，高度模拟真实考试节奏。 * **3 套按知识领域加权分组：** 题目数量根据 CDMP DMF 考试各知识领域的权重分配，您可以优先攻克关键领域。 **课程特点：** * **题型与考试一致：** 每道题均为多项选择题，包含一个正确答案和四个干扰项。 * **覆盖 DMBOKv2 所有知识领域：** 涵盖数据管理、数据处理伦理、数据治理、数据架构、数据建模与设计、数据存储与运行、数据安全、数据集成与互操作性、文档与内容管理、参考与主数据、数据仓库与商业智能及大数据、元数据、数据质量等章节。 * **难度梯度：** 题目难度多样，确保您为各种挑战做好准备。 **学习建议：** 1. **完成全部练习测试**，全面评估您的掌握程度。 2. **重点关注薄弱的知识领域**，加深理解。 3. **如需进一步学习**，Modelware 还提供各知识领域的总结笔记和复习视频。 通过本课程的系统训练，您将显著提升应试能力，为成功通过 CDMP 数据管理考试做好充分准备。
These practice questions have been designed to test your knowledge and get a sense of the type of questions seen in the exam. The faster you are able to answer everything right, the readier you will be for the exam. There are 6 practice tests, 3 mimic the exam with 1min per question, 3 are grouped by knowledge area weighting (i.e. number of questions regarding the knowledge area in the the CDMP DMF Exam). Make sure you focus on the high contributing knowledge areas first.The questions are in the same format as the exam. Each question is multiple choice with one correct answer and 4 distractors. Work through the full practice tests and then focus on knowledge areas that you need to understand more clearly. Modelware also provides a course for the summary notes and refresher videos of each knowledge area if you need more information.This course contains questions from each knowledge area and the below chapters of the DMBOKv2. The questions are mimic to the exam and range in different difficulty levels to ensure you are ready.Chapter 1 Data Management:Chapter 2 Data Handling EthicsChapter 3 Data GovernanceChapter 4 Data ArchitectureChapter 5 Data Modelling & DesignChapter 6 Data Storage & OperationsChapter 7 Data SecurityChapter 8 Data Integration & InteroperabilityChapter 9 Document & Content ManagementChapter 10 Reference & Master DataChapter 11 & 14 DW & BI and Big DataChapter 12 MetadataChapter 13 Data Quality
