Microsoft Implementing a Data Warehouse using SQL (MCSE Data Management and Analytics) Exam Syllabus

Implementing a Data Warehouse using SQL PDF, 70-767 Dumps, 70-767 PDF, Implementing a Data Warehouse using SQL VCE, 70-767 Questions PDF, Microsoft 70-767 VCE, Microsoft MCSE Data Management and Analytics Dumps, Microsoft MCSE Data Management and Analytics PDFUse this quick start guide to collect all the information about Microsoft Implementing a Data Warehouse using SQL (70-767) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the 70-767 Microsoft Implementing a Data Warehouse using SQL exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCSE Data Management and Analytics certification exam.

The Microsoft Implementing a Data Warehouse using SQL certification is mainly targeted to those candidates who want to build their career in Microsoft SQL Server domain. The Microsoft Certified Solutions Expert (MCSE) -Data Management and Analytics exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCSE Data Management and Analytics.

Microsoft Implementing a Data Warehouse using SQL Exam Summary:

Exam Name Microsoft Certified Solutions Expert (MCSE) -Data Management and Analytics
Exam Code 70-767
Exam Price $165 (USD)
Duration 150 mins
Number of Questions 40-60
Passing Score 700 / 1000
Books / Training 20767: Implementing a SQL Data Warehouse (five days)
Schedule Exam Pearson VUE
Sample Questions Microsoft Implementing a Data Warehouse using SQL Sample Questions
Practice Exam Microsoft 70-767 Certification Practice Exam

Microsoft 70-767 Exam Syllabus Topics:

Topic Details Weights
Design, implement, and maintain a data warehouse

Design and implement dimension tables
- Design shared and conformed dimensions, determine support requirements for slowly changing dimensions, determine attributes, design hierarchies, determine star or snowflake schema requirements, determine the granularity of relationship by using fact tables, determine auditing or lineage requirements, determine keys and key relationships for a data warehouse, implement dimensions, implement data lineage of a dimension table

Design and implement fact tables
- Identify measures, identify dimension table relationships, create composite keys, design a data warehouse that supports many-to-many relationships, implement semi-additive measures, implement non-additive measures

Design and implement indexes for a data warehouse workload
- Design an indexing solution; select appropriate indexes; implement clustered, non-clustered, filtered, and columnstore indexes

Design storage for a data warehouse
- Design an appropriate storage solution, including hardware, disk, and file layout

Design and implement partitioned tables and views
- Design a partition structure to support a data warehouse, implement sliding windows, implement partition elimination, design a partition structure that supports the quick loading and scale-out of data

35-40%
Extract, transform, and load data

Design and implement an extract, transform, and load (ETL) control flow by using a SQL Server Integration Services (SSIS) package
- Design and implement ETL control flow elements, including containers, tasks, and precedence constraints; create variables and parameters; create checkpoints, sequence and loop containers, and variables in SSIS; implement data profiling, parallelism, transactions, logging, and security

Design and implement an ETL data flow by using an SSIS package
- Implement slowly changing dimension, fuzzy grouping, fuzzy lookup, audit, blocking, non-blocking, and term lookup transformations; map columns; determine the appropriate transform object for a given task; determine appropriate scenarios for Transact-SQL joins versus SSIS lookup; design table loading by using bulk loading or standard loading; remove extra rows or bad rows by using deduplication

Implement an ETL solution that supports incremental data extraction
- Design fact table patterns, enable Change Data Capture, create a SQL MERGE statement

Implement an ETL solution that supports incremental data loading
- Design a control flow to load change data, load data by using Transact-SQL Change Data Capture functions, load data by using Change Data Capture in SSIS

Debug SSIS packages
- Fix performance, connectivity, execution, and failed logic issues by using the debugger; enable logging for package execution; implement error handling for data types; implement breakpoints; add data viewers; profile data with different tools; perform batch clean-up

Deploy and configure SSIS packages and projects
- Create an SSIS catalog; deploy packages by using the deployment utility, SQL Server, and file systems; run and customize packages by using DTUTIL

40-45%
Build data quality solutions

Create a knowledge base
- Create a Data Quality Services (DQS) knowledge base, determine appropriate use cases for a DQS knowledge base, perform knowledge discovery, perform domain management

Maintain data quality by using DQS
- Add matching knowledge to a knowledge base, prepare a DQS for data deduplication, create a matching policy, clean data by using DQS knowledge clean data by using the SSIS DQS task, install DQS

Implement a Master Data Services (MDS) model
- Install MDS; implement MDS; create models, entities, hierarchies, collections, and attributes; define security roles; import and export data; create and edit a subscription; implement entities, attributes, hierarchies, and business rules

Manage data by using MDS
- Use MDS tools, use the Master Data Services Configuration Manager, create a Master Data Manager database and web application, deploy a sample model using MDSModelDeploy.exe, use the Master Data Services web application, use the Master Data Services Add-in for Excel, create a Master Data Management hub, stage and load data, create subscription views

15-20%

To ensure success in Microsoft MCSE Data Management and Analytics certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Implementing a Data Warehouse using SQL (70-767) exam.

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