MCSA 2016 Certification Training : SQL Server Developer, Database Administrator (DBA), Business Intelligence (MSBI)

Query data by using subqueries and APPLY

• Determine the results of queries using subqueries and table joins, evaluate performance differences between table joins and correlated subqueries based on provided data and query plans, distinguish between the use of CROSS APPLY and OUTER APPLY, write APPLY statements that return a given data set based on supplied data

Query data by using table expressions

• Identify basic components of table expressions, define usage differences between table expressions and temporary tables, construct recursive table expressions to meet business requirements

Group and pivot data by using queries

• Use windowing functions to group and rank the results of a query; distinguish between using windowing functions and GROUP BY; construct complex GROUP BY clauses using GROUPING SETS, and CUBE; construct PIVOT and UNPIVOT statements to return desired results based on supplied data; determine the impact of NULL values in PIVOT and UNPIVOT queries

Query temporal data and non-relational data

• Query historic data by using temporal tables, query and output JSON data, query and output XML data

Create database programmability objects by using Transact-SQL

• Create stored procedures, table-valued and scalar-valued user-defined functions, and views; implement input and output parameters in stored procedures; identify whether to use scalar-valued or table-valued functions; distinguish between deterministic and non-deterministic functions; create indexed views

Implement error handling and transactions

• Determine results of Data Definition Language (DDL) statements based on transaction control statements, implement TRY…CATCH error handling with Transact-SQL, generate error messages with THROW and RAISERROR, implement transaction control in conjunction with error handling in stored procedures

Implement data types and NULLs

• Evaluate results of data type conversions, determine proper data types for given data elements or table columns, identify locations of implicit data type conversions in queries, determine the correct results of joins and functions in the presence of NULL values, identify proper usage of ISNULL and COALESCE functions

Design and implement a relational database schema

• Design tables and schemas based on business requirements, improve the design of tables by using normalization, write table create statements, determine the most efficient data types to use

Design and implement indexes

• Design new indexes based on provided tables, queries, or plans; distinguish between indexed columns and included columns; implement clustered index columns by using best practices; recommend new indexes based on query plans

Design and implement views

• Design a view structure to select data based on user or business requirements, identify the steps necessary to design an updateable view, implement partitioned views, implement indexed views

Implement columnstore indexes

• Determine use cases that support the use of columnstore indexes, identify proper usage of clustered and non-clustered columnstore indexes, design standard non-clustered indexes in conjunction with clustered columnstore indexes, implement columnstore index maintenance

Ensure data integrity with constraints

• Define table and foreign key constraints to enforce business rules, write Transact-SQL statements to add constraints to tables, identify results of Data Manipulation Language (DML) statements given existing tables and constraints, identify proper usage of PRIMARY KEY constraints

Create stored procedures

• Design stored procedure components and structure based on business requirements, implement input and output parameters, implement table-valued parameters, implement return codes, streamline existing stored procedure logic, implement error handling and transaction control logic within stored procedures

Create triggers and user-defined functions

• Design trigger logic based on business requirements; determine when to use Data Manipulation Language (DML) triggers, Data Definition Language (DDL) triggers, or logon triggers; recognize results based on execution of AFTER or INSTEAD OF triggers; design scalar-valued and table-valued user-defined functions based on business requirements; identify differences between deterministic and non-deterministic functions

Implement transactions

• Identify DML statement results based on transaction behavior, recognize differences between and identify usage of explicit and implicit transactions, implement savepoints within transactions, determine the role of transactions in high-concurrency databases

Manage isolation levels

• Identify differences between Read Uncommitted, Read Committed, Repeatable Read, Serializable, and Snapshot isolation levels; define results of concurrent queries based on isolation level; identify the resource and performance impact of given isolation levels

Optimize concurrency and locking behavior

• Troubleshoot locking issues, identify lock escalation behaviors, capture and analyze deadlock graphs, identify ways to remediate deadlocks

Implement memory-optimized tables and native stored procedures

• Define use cases for memory-optimized tables versus traditional disk-based tables, optimize performance of in-memory tables by changing durability settings, determine best case usage scenarios for natively compiled stored procedures, enable collection of execution statistics for natively compiled stored procedures

Optimize statistics and indexes

• Determine the accuracy of statistics and the associated impact to query plans and performance, design statistics maintenance tasks, use dynamic management objects to review current index usage and identify missing indexes, consolidate overlapping indexes

Analyze and troubleshoot query plans

• Capture query plans using extended events and traces, identify poorly performing query plan operators, create efficient query plans using Query Store, compare estimated and actual query plans and related metadata, configure Azure SQL Database Performance Insight

Manage performance for database instances

• Manage database workload in SQL Server; design and implement Elastic Scale for Azure SQL Database; select an appropriate service tier or edition; optimize database file and tempdb configuration; optimize memory configuration; monitor and diagnose scheduling and wait statistics using dynamic management objects; troubleshoot and analyze storage, IO, and cache issues; monitor Azure SQL Database query plans

Monitor and trace SQL Server baseline performance metrics

• Monitor operating system and SQL Server performance metrics; compare baseline metrics to observed metrics while troubleshooting performance issues; identify differences between performance monitoring and logging tools, such as perfmon and dynamic management objects; monitor Azure SQL Database performance; determine best practice use cases for extended events; distinguish between Extended Events targets; compare the impact of Extended Events and SQL Trace; define differences between Extended Events Packages, Targets, Actions, and Sessions

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

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

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 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

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

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

Design and implement SQL Server Database security

• Configure firewalls; manage logins, users, and roles; assign permissions; configure auditing; configure Transparent Database Encryption (TDE); configure row-level security; configure data encryption; configure data masking; configure Always Encrypted

Design and implement Azure SQL Database security

• Configure firewalls; manage logins, users, and roles; assign permissions; configure auditing; configure row-level security; configure data encryption; configure data masking; configure Always Encrypted, configure Automatic Threat Detection

Design and implement high availability solutions

• Design a high-availability solution topology, design a high-availability solution for SQL on Azure VMs, implement high-availability solutions

Design and implement scalable solutions

• Design a scale-out solution, implement multi-master scenarios with database replication, implement elastic scale for Azure SQL Database

Design and implement Azure SQL Database data recovery

• Implement self-service restore, copy and export databases, implement long-term retention backups

Monitor and troubleshoot SQL Server VMs on Azure

• Monitor database and instance activity, monitor by using DMVs and DMFs, monitor performance and scalability

Monitor and troubleshoot SQL Database

• Monitor and troubleshoot SQL Database, monitor database activity, monitor by using DMVs and DMFs, monitor performance and scalability

Automate and manage database implementations on Azure

• Automate and manage SQL Server on Azure VMs, automate and manage Azure SQL Database, configure automation and runbooks

Create and optimize data models

• Manage data relationships, optimize models for reporting, manually enter data, Use Power Query

Create calculated columns, tables, and measures

• Create DAX queries for calculated columns, tables, and measures

Create performance KPIs

• Calculate the actual, calculate the target, calculate actual to target

Create hierarchies

• Use date hierarchies, use business hierarchies, resolve hierarchy issues

Create and format interactive visualizations

• Select a visualization type, configure page layout and formatting, setup visual relationships, configure duplicate pages, handle categories that have no data, setup default summaries and categories, position, align, and sort visuals, enable and integrate R visuals, format calculated measures

Manage custom reporting solutions

• Use Power BI API, use Microsoft Power BI Embedded, enable developers to create and edit dashboards through custom applications, enable developers to embed dashboards in applications, authenticate a Power BI web application, enable developers to create custom visuals

Configure a dashboard

• Connect to the Power BI service, publish connections to services by using SQL Server Analysis Services (SSAS), publish visualizations including dataConfigure a dashboard, add text and images, filter dashboards, configure dashboard settings, customize the URL and title, enable natural language queries

Publish dashboards

• Publish to web, publish to Microsoft SharePoint, publish to SQL Server Reporting Services (SSRS)

Configure security for dashboards

• Create a security group by using the Admin Portal, share dashboard with users or security groups, integrate with Microsoft OneDrive for Business, configure row-level Security, configure gateways