70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012/2014

Exam Name: Implementing a Data Warehouse with Microsoft SQL Server 2012/2014

Exam Code: 70-463

Try Online Exam »

Microsoft 70-463 Exam Summary:

Exam Name Implementing a Data Warehouse with Microsoft SQL Server 2012/2014
Exam Code  70-463
Exam Duration  120 minutes
Exam Questions  40-60
Passing Score  700 out of 1000
Exam Price   $165 (USD)
Books 20463C: Implementing a Data Warehouse with Microsoft SQL Server (five days)
Sample Questions   Implementing a Data Warehouse with Microsoft SQL Server Certification Sample Question
Practice Exam   Implementing a Data Warehouse with Microsoft SQL Server Certification Practice Exam

Microsoft 70-463 Exam Topics:

Objective Details 
Design and implement a data warehouse (10–15%)
Design and implement dimensions

– Design shared/conformed dimensions; determine if you need support for slowly changing dimensions; determine attributes; design hierarchies; determine whether you need star or snowflake schema; determine the granularity of relationship with fact tables; determine the need for auditing or lineage; determine keys (business transactional or your own data warehouse/surrogate keys); implement dimensions; implement data lineage of a dimension table

Design and implement fact tables – Design a data warehouse that supports many to many relationships; appropriately index a fact table; using columnstore indexes; partitioning; additive measures; semi-additive measures; non-additive measures; implement fact tables; determining the loading method for the fact tables; implement data lineage of a fact table; design summary aggregation tables
Extract and transform data (20–25%)
Define connection managers

– Plan the configuration of connection managers; package level or project level connection manager; define a connection string; parameterization of connection strings

Design data flow – Define data sources and destinations; distinguish blocking and non-blocking transformations; use different methods to pull out changed data from data sources; determine appropriate data flow components; determine the need for supporting Slowly Changing Dimensions (SCD); determine whether to use SQL Joins or SSIS lookup or merge join transformations; batch processing versus row by row processing; determine the appropriate transform to use for a specific task; determine the need and method for identity mapping and deduplicating; fuzzy lookup, fuzzy grouping and Data Quality Services (DQS) transformation; determine the need for custom data sources, destinations, and transforms; determine what to do with erroneous rows; determine auditing needs; trusted/authoritative data sources, including warehouse metadata; extracting only changed rows
Implement data flow – Debug data flow; use the appropriate data flow components; SQL / SSIS data transformation; create SSIS packages that support slowly changing dimensions; use the lookup task in SSIS; map identities using SSIS fuzzy lookup (advanced); specify a data source and destination; use data flows; different categories of transformations; read, transform and load data; understand which transforms to use to accomplish a specific business task; data correction transformation; performance tune an SSIS dataflow; optimize Integration Services packages for speed of execution; maintain data integrity, including good data flow
Manage SSIS package execution – Schedule package execution by using SQL Server Agent; execute packages by using DTEXEC; execute packages by using SQL Server Management Studio; implement package execution; plan and design package execution strategy; use PowerShell to execute script; monitor the execution using Management Studio; use DTEXECUI; ETL relatability 
Implement script tasks in SSIS – Determine if it is appropriate to use a script task; extending the capability of control flow; perform a custom action as needed (not on every row) during a control flow 
Load data (25–30%) 
Design control flow – Determine control flow; determine containers and tasks needed; determine precedence constraints; design an SSIS package strategy with rollback, staging and transaction control; decide between one package or multiple packages; determine event handlers; determine variables; determine parameters on package and project level; determine connection managers and whether they are package or project level; determine the need for custom tasks; determine how much information you need to log from a package; determine the need for checkpoints; determine security needs 
Implement package logic by using SSIS variables and parameters – User variables; variable scope, data type; implement parameterization of properties using variables; using variables in precedence constraints; referring to SSIS system variables; design dynamic SSIS packages; package configurations (file or SQL tables); expressions; package and project parameters; project-level connection managers; variables; implement dynamic package behavior; configure packages in SSIS for different environments, package configurations (XML configuration file, SQLServer table, registry entry; parent package variables, environment variable); parameters (package and project level); project connection managers; property expressions (use expressions for connection managers) 
Implement control flow – Checkpoints; debug control flow; implement the appropriate control flow task to solve a problem; data profiling; use sequence containers and loop containers; manage transactions in SSIS packages; managing parallelism; using precedence constraint to control task execution sequence; creating package templates; using the execute package task 
Implement data load options – Implement a full and incremental data load strategy; plan for incremental update of the relational Data Mart; plan for loads into indexed tables; configure appropriate bulk load options; select an appropriate load technique (SSIS Destination versus T-SQL) and load partitioned tables 
Implement script components in SSIS – Create an SSIS package that handles SCD Type 2 changes without using the SCD component; work with script component in SSIS; deciding when it is appropriate to use a script component versus a built-in; source, transformation, destination component; use cases: web service source and destination, getting the error message 
Configure and deploy SSIS solutions (20–25%)
Troubleshoot data integration issues – Performance issues; connectivity issues; execution of a task or transformation failed; logic issues; demonstrate awareness of the new SSIS logging infrastructure; troubleshoot a failed package execution to determine the root cause of failure; troubleshoot SSIS package failure from an invalid datatype; implement breakpoints; data viewers; profile data with different tools; batch cleanup 
Install and maintain SSIS components – Software installation (IS, management tools); development box and server; install specifics for remote package execution; planning for installation (32- versus 64-bit); upgrade; provisioning the accounts; creating the catalog 
Implement auditing, logging, and event handling – Audit package execution by using system variables; propagate events; use log providers; log an SSIS execution; create alerting and notification mechanisms; use Event Handlers in SSIS to track ETL events and errors; implement custom logging 
Deploy SSIS solutions – Create and configure an SSIS catalog; deploy SSIS packages by using the deployment utility; deploy SSIS packages to SQL or file system locations; validate deployed packages; deploy packages on multiple servers; how to install custom components and tasks; deploy SSIS packages by using DTUTIL 
Configure SSIS security settings – SSIS catalog database roles; package protection levels; secure Integration Services packages that are deployed at the file system; secure Integration Services parameters, configuration 
Build data quality solutions (15–20%)
Install and maintain data quality services – Installation prerequisites; .msi package; adding users to the DQ roles; identity analysis, including data governance 
Implement master data management solutions – Install Master Data Services (MDS); implement MDS; create models, entities, hierarchies, collections, attributes; define security roles; import/export; subscriptions 
Create a data quality project to clean data – Profile Online Transaction Processing (OLTP) and other source systems; data quality knowledge base management; create data quality project; use data quality client; improve data quality; identity mapping and deduplicating; handle history and data quality; manage data quality/cleansing