To help you prepare for this exam, our instructor-led course 10777 describes how to implement a BI platform to support information worker analytics. In this course students will learn how to create a data warehouse with SQL Server 2012, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.
Course 10777 helps people prepare for the exam 70-463.
The primary audience for this exam is ETL and Data Warehouse Developers. They are most likely to focus on hands-on work creating business intelligence (BI) solutions including data cleansing, Extract Transform Load (ETL), and Data Warehouse implementation. Their primary responsibilities may include:
- Mastering data using Master Data Services
- Cleansing data using Data Quality Services
- ETL Package development for data loading/transfer/transformation
- Data Synchronization
- One-time data or application migration
- Ad-hoc data manipulations, transformations
- Scheduling ETL jobs
- Managing, configuring and deploying SQL Server Integration Services (SSIS) packages
Credit Toward Certification
Exam 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012: counts as credit toward the following certification(s):
- MCSA: SQL Server 2012
- MCSE: Data Platform
- MCSE: Business Intelligence
Skills Being 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.The information after “This objective may include but is not limited to” is intended to further define or scope the objective by describing the types of skills and topics that may be tested for the objective. However, it is not an exhaustive list of skills and topics that could be included on the exam for a given skill area. You may be tested on other skills and topics related to the objective that are not explicitly listed here.
Design and Implement a Data Warehouse (11%)
- Design and implement dimensions.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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 (23%)
- Define connection managers.
- This objective may include but is not limited to: plan the configuration of connection managers; package level or project level connection manager; define a connection string; parameterization of connection strings
- Design data flow.
- This objective may include but is not limited to: 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 text mining; determine the need for custom data sources, destinations, and transforms; determine what to do with erroneous rows; determine auditing needs; determine sampling needs for data mining (advanced); trusted/authoritative data sources, including warehouse metadata
- Implement data flow.
- This objective may include but is not limited to: 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 & 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.
- This objective may include but is not limited to: 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 Manangement Studio; use DTEXECUI; ETL restartability
- Implement script tasks in SSIS.
- This objective may include but is not limited to: determine if it is appropriate to use a script task; extending the capability of a control flow; perform a custom action as needed (not on every row) during a control flow
Load Data (27%)
- Design control flow.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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 (xmlconfiguration 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.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: implement a full and incremental data load strategy; plan for an incremental update of the relational Data Mart
- Implement script components in SSIS.
- This objective may include but is not limited to: 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 (24%)
- Troubleshoot data integration issues.
- This objective may include but is not limited to: performance issues; connectivity issues; execution of a task or tranformation 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 break points; data viewers; profile data with different tools; batch cleanup
- Install and maintain SSIS components.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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%)
- Install and maintain Data Quality Services.
- This objective may include but is not limited to: installation prerequisites; .msi package; adding users to the DQ roles; identity analysis, including data governance
- Implement master data management solutions.
- This objective may include but is not limited to: 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.
- This objective may include but is not limited to: 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