Exam 70-765 Provisioning SQL Databases (beta)

Published: August 26, 2016
Languages: English
Audiences: IT professionals
Technology: SQL Server
Credit toward certification: MCP, MCSA

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

Please note that the questions may test on, but will not be limited to, the topics described in the bulleted text.

Do you have feedback about the relevance of the skills measured on this exam? Please send Microsoft your comments. All feedback will be reviewed and incorporated as appropriate while still maintaining the validity and reliability of the certification process. Note that Microsoft will not respond directly to your feedback. We appreciate your input in ensuring the quality of the Microsoft Certification program.

If you have concerns about specific questions on this exam, please submit an exam challenge.

If you have other questions or feedback about Microsoft Certification exams or about the certification program, registration, or promotions, please contact your Regional Service Center.

Implement SQL in Azure (30–35%)
Deploy a Microsoft Azure SQL Database
Choose a service tier, create servers and databases, create a sysadmin account, configure elastic pools
Plan for SQL Server installation
Plan for an IaaS or on-premises deployment, select the appropriate size for a virtual machine, plan storage pools based on performance requirements, evaluate best practices for installation, design a storage layout for a SQL Server virtual machine
Deploy SQL Server instances
Deploy a SQL Server instance in IaaS and on-premises, manually install SQL Server on an Azure Virtual Machine, provision an Azure Virtual Machine to host a SQL Server instance, automate the deployment of SQL Server databases, deploy SQL Server by using templates

Manage databases and instances (35–40%)
Configure secure access to Microsoft Azure SQL Databases
Configure firewall rules, configure Always Encrypted for Azure SQL Database, configure cell-level encryption, configure dynamic data masking, configure transparent data encryption (TDE)
Configure SQL Server performance settings
Configure database performance settings, configure max server memory, configure the database scope, configure operators and alerts
Manage SQL Server instances
Create databases, manage files and file groups, manage system database files, configure tempdb

Deploy and migrate applications (30–35%)
Deploy applications to Microsoft Azure SQL Database
Manage deployments that support multiple tenants, migrate on-premises SQL Server to Azure SQL Database, migrate data to Azure SQL Database
Deploy applications to SQL Server on Azure Virtual Machines
Migrate an on-premises SQL Server database to an Azure Virtual Machine, generate benchmark data for performance needs, perform performance tuning on Azure IaaS, support availability sets in Azure
Migrate client applications
Configure application connection strings, manage traffic between on-premises applications and Azure services, develop application retry connection logic, identify application patterns that must be migrated with SQL Server data, evaluate network performance between applications and databases

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Exam 70-767 Implementing a SQL Data Warehouse (in development)

Published: October 18, 2016
Languages: English
Audiences: IT professionals
Technology: SQL Server
Credit toward certification: MCP, MCSA

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

Please note that the questions may test on, but will not be limited to, the topics described in the bulleted text.

Do you have feedback about the relevance of the skills measured on this exam? Please send Microsoft your comments. All feedback will be reviewed and incorporated as appropriate while still maintaining the validity and reliability of the certification process. Note that Microsoft will not respond directly to your feedback. We appreciate your input in ensuring the quality of the Microsoft Certification program.

If you have concerns about specific questions on this exam, please submit an exam challenge.

If you have other questions or feedback about Microsoft Certification exams or about the certification program, registration, or promotions, please contact your Regional Service Center.

Design and implement a data warehouse (30–35%)
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

Extract, transform, and load data (35–40%)
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

Integrate solutions with cloud data and big data (15–20%)
Integrate external data sources with SQL Server by using Polybase
Integrate with Hadoop, integrate with text files stored in the Azure Blob service, manage external tables, access data in Hadoop databases with Transact-SQL, access data in the Azure Blob service by using Transact-SQL, import data from Hadoop or blobs as regular SQL Server tables, export data to Hadoop or the Azure Blob service
Extract, transform, and load data from SQL Data Warehouse by using Polybase
Integrate Azure SQL Data Warehouse with on-premises data warehouses, implement bi-directional data synchronization between Azure and on-premises systems, load data into SQL Data Warehouse from Polybase, design an incremental load strategy by using Polybase and the Azure Blob service
Design and implement an Azure SQL Data Warehouse
Create a new Azure SQL Data Warehouse database by using the Azure portal, create an Azure SQL Data Warehouse database by using Transact-SQL, select the appropriate method to load data into Azure SQL Data Warehouse
Manage and maintain a SQL Data Warehouse
Manage queries by using labels; manage statistics; manage partition distribution; scale out the data warehouse; grow, shrink, and pause the data warehouse

Build data quality solutions (15–20%)
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

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Exam 70-768 Developing SQL Data Models (beta)

Published: August 26, 2016
Languages: English
Audiences:IT professionals, Developers
Technology: SQL Server
Credit toward certification: MCP, MCSA

Skills measured
This exam measures your ability to accomplish the technical tasks listed below. The percentages indicate the relative weight of each major topic area on the exam. The higher the percentage, the more questions you are likely to see on that content area on the exam. View video tutorials about the variety of question types on Microsoft exams.

Please note that the questions may test on, but will not be limited to, the topics described in the bulleted text.
Do you have feedback about the relevance of the skills measured on this exam? Please send Microsoft your comments. All feedback will be reviewed and incorporated as appropriate while still maintaining the validity and reliability of the certification process. Note that Microsoft will not respond directly to your feedback. We appreciate your input in ensuring the quality of the Microsoft Certification program.
If you have concerns about specific questions on this exam, please submit an exam challenge.
If you have other questions or feedback about Microsoft Certification exams or about the certification program, registration, or promotions, please contact your Regional Service Center.

Design a multidimensional business intelligence (BI) semantic model (25–30%)
Create a multidimensional database by using Microsoft SQL Server Analysis Services (SSAS)
Design, develop, and create multidimensional databases; select a storage model
Design and implement dimensions in a cube
Select an appropriate dimension model, such as fact, parent-child, roleplaying, reference, data mining, many-to-many, and slowly changing dimension; implement a dimension type; define attribute relationships
Implement measures and measure groups in a cube
Design and implement measures, measure groups, granularity, calculated measures, and aggregate functions; define semi-additive behavior

Design a tabular BI semantic model (20–25%)
Design and publish a tabular data model
Design measures, relationships, hierarchies, partitions, perspectives, and calculated columns; create a time table; publish from Microsoft Visual Studio; import from Microsoft PowerPivot; select a deployment option, including Processing Option, Transactional Deployment, and Query Mode
Configure, manage, and secure a tabular model
Configure tabular model storage and data refresh, configure refresh interval settings, configure user security and permissions, configure row-level security
Develop a tabular model to access data in near real time
Use DirectQuery with Oracle, Teradata, Excel, and PivotTables; convert in-memory queries to DirectQuery
Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX) (15–20%)
Create basic MDX queries
Implement basic MDX structures and functions, including tuples, sets, and TopCount
Implement custom MDX solutions
Create custom MDX or logical solutions for pre-prepared case tasks or business rules, define a SCOPE statement
Create formulas by using the DAX language
Use the EVALUATE and CALCULATE functions, filter DAX queries, create calculated measures, perform data analysis by using DAX

Configure and maintain SQL Server Analysis Services (SSAS) (30–35%)

Plan and deploy SSAS
Configure memory limits, configure Non-Union Memory Architecture (NUMA), configure disk layout, determine SSAS instance placement
Monitor and optimize performance
Monitor performance and analyze query plans by using Extended Events and Profiler, identify bottlenecks in SSAS queries, monitor processing and query performance, resolve performance issues, configure usability limits, optimize and manage model design
Configure and manage processing
Configure partition processing; configure dimension processing; use Process Default, Process Full, Process Clear, Process Data, Process Add, Process Update, Process Index, Process Structure, and Process Clear Structure processing methods; configure Parallel, Sequential, and Writeback processing settings
Create Key Performance Indicators (KPIs) and translations
Configure KPI options, including Associated measure group, Value Expression, Goal Expression, Status, Status expression, Trend, Trend expression, and Weight; create KPIs in multidimensional models and tabular models; create and develop translations

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