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


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MCSA - SQL Server Development. Paper 1
EXAM 70-761 (Querying Data with Transact-SQL)

 
    • Identify proper SELECT query structure, write specific queries to satisfy business requirements, construct results from multiple queries using set operators, distinguish between UNION and UNION ALL behaviour, identify the query that would return expected results based on provided table structure and/or data
  •  
  • Query multiple tables by using joins
    • Write queries with join statements based on provided tables, data, and requirements; determine proper usage of INNER JOIN, LEFT/RIGHT/FULL OUTER JOIN, and CROSS JOIN; construct multiple JOIN operators using AND and OR; determine the correct results when presented with multi-table SELECT statements and source data; write queries with NULLs on joins
  •  
  • Implement functions and aggregate data
    • Construct queries using scalar-valued and table-valued functions; identify the impact of function usage to query performance and WHERE clause sargability; identify the differences between deterministic and non-deterministic functions; use built-in aggregate functions; use arithmetic functions, date-related functions, and system functions
  •  
  • Modify data
    • Write INSERT, UPDATE, and DELETE statements; determine which statements can be used to load data to a table based on its structure and constraints; construct Data Manipulation Language (DML) statements using the OUTPUT statement; determine the results of Data Definition Language (DDL) statements on supplied tables and data
  • 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

MCSA - SQL Server Development. Paper 2:
EXAM 70-762 (Developing SQL Databases)

 
  • 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

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MCSA - SQLDBA Paper 1: EXAM 70-764 (Administering a SQL Database Infrastructure)

 
  • Configure encryption
    • Implement cell-level encryption, implement Always Encrypted, implement backup encryption, configure transparent data encryption, configure encryption for connections, troubleshoot encryption errors
  • Configure data access and permissions
    • Manage database object permissions, create and maintain users, create and maintain custom roles, configure user options for Azure SQL Database, configure row-level security, configure dynamic data masking
  • Configure auditing
    • Configure an audit on SQL Server, query the SQL Server audit log, manage a SQL Server audit, configure an Azure SQL Database audit, analyze audit logs and reports from Azure SQL Database
  • Develop a backup strategy
    • Back up very large databases, configure alerting for failed backups, back up databases to Azure, manage transaction log backups, configure database recovery models, configure backup automation
  • Restore databases
    • Perform piecemeal restores, perform page recovery, perform point-in-time recovery, restore file groups, develop a plan to automate and test restores
  • Manage database integrity
    • Implement database consistency checks, identify database corruption, recover from database corruption
  • Monitor database activity
    • Monitor current sessions, identify sessions that cause blocking activity, identify sessions that consume tempdb resources, configure the data collector
  • Monitor queries
    • Manage the Query Store, configure Extended Events and trace events, identify problematic execution plans, troubleshoot server health using Extended Events
  • Manage indexes
    • Identify and repair index fragmentation, identify and create missing indexes, identify and drop underutilized indexes, manage existing columnstore indexes
  • Manage statistics
    • Identify and correct outdated statistics, implement Auto Update Statistics, implement statistics for large tables
  • Monitor SQL Server instances
    • Create and manage operators, create and manage SQL Agent alerts, define custom alert actions, define failure actions, configure database mail, configure Policy-Based Management, identify available space on data volumes, identify the cause of performance degradation
  • Implement log shipping
    • Configure log shipping, monitor log shipping
  • Implement AlwaysOn Availability Groups
    • Configure Windows clustering, create an availability group, configure read-only routing, manage failover, create distributed availability groups
  • Implement failover cluster instances
    • Manage shared disks, configure cluster shared volumes

MCSA - SQLDBA Paper 2: EXAM 70-765 (Provisioning SQL Databases)

 
  • 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
  • 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 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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MCSA - MSBI Paper 1: EXAM 70-767 (Implementing an SQL 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
  • 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 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
  • 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

MCSA - MSBI Paper 2: EXAM 70-768 (Developing SQL Data Models)

 
 
  • 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

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