Microsoft 70-463 EXAM QUESTIONS & ANSWERS

Exam Code: 70-463
Exam Name: Implementing a Data Warehouse with Microsoft SQL Server 2012
Questions: 111 Q&As
Updated: 2018-12-03
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Exam 70-463
Implementing a Data Warehouse with Microsoft SQL Server 2012

Language(s): English, French, German, Japanese, Russian, Spanish, Portuguese (Brazil), Chinese (Simplified)
Audience(s): IT professionals
Technology: Microsoft SQL Server 2012
Credit toward certification: MCSA, MCSE

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Design and implement a data warehouse (11%)

  • 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 (23%)
  • 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 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

    • 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 Manangement Studio; use DTEXECUI; ETL restartability

  • Implement script tasks in SSIS

    • 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

    • 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 (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

    • 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 an incremental update of the relational Data Mart

  • 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 (24%)
  • Troubleshoot data integration issues

    • 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

    • 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%)
  • 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

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