Exam Ref 70-767 Implementing a SQL Data Warehouse
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Prepare for Microsoft Exam 70-767-and help demonstrate your real-world mastery of skills for managing data warehouses. This exam is intended for Extract, Transform, Load (ETL) data warehouse developers who create business intelligence (BI) solutions. Their responsibilities include data cleansing as well as ETL and data warehouse implementation. The reader should have experience installing and implementing a Master Data Services (MDS) model, using MDS tools, and creating a Master Data Manager database and web application. The reader should understand how to design and implement ETL control flow elements and work with a SQL Service Integration Services package.
Focus on the expertise measured by these objectives: - Design, and implement, and maintain a data warehouse - Extract, transform, and load data - Build data quality solutionsThis Microsoft Exam Ref - Organizes its coverage by exam objectives - Features strategic, what-if scenarios to challenge you - Assumes you have working knowledge of relational database technology and incremental database extraction, as well as experience with designing ETL control flows, using and debugging SSIS packages, accessing and importing or exporting data from multiple sources, and managing a SQL data warehouse. Implementing a SQL Data Warehouse About the Exam Exam 70-767 focuses on skills and knowledge required for working with relational database technology. About Microsoft Certification Passing this exam earns you credit toward a Microsoft Certified Professional (MCP) or Microsoft Certified Solutions Associate (MCSA) certification that demonstrates your mastery of data warehouse management Passing this exam as well as Exam 70-768 (Developing SQL Data Models) earns you credit toward a Microsoft Certified Solutions Associate (MCSA) SQL 2016 Business Intelligence (BI) Development certification. See full details at: microsoft.com/learningJose Chinchilla is the President of Agile Bay. He writes and speaks about SQL Server administration and performance, data warehousing, business intelligence, predictive analytics and big data.
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