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微软MCTS认证70-452考试大纲,学习建议,考点及题型

2008-11-10 06:21    作者:曹前    来源:IT认证考试资源网    浏览:

考试编号:70-452
所属认证:MCITP
考试时间:120分钟
考试题量:50-60道
通过分数:700分,总分1000分
考试语言:英语
考试机构:Prometric
考试题型:选择题,拖图题
技术种类:SQL 2008
职业方向:数据库工程师
考试费用:每门考试50美元,折合人民币330元左右
资料费用:http://shop.itexamprep.com/


 

微软认证考试70-452的相关考点和考试大纲内容要求

Designing and Managing Reports (20 percent)

Design report architecture.
This objective may include but is not limited to: linked reports, drill-down reports, drill-through reports, migration strategies, access report services API, sub reports, Code-Behind strategies
 
Design a data acquisition strategy.
This objective may include but is not limited to: data query parameters, creating appropriate SQL queries for an application (MDX queries), managing data rights and security, extracting data from Analysis Services, balancing query-based processing vs. filter-based processing, managing data sets through the use of stored procedures
 
Define a report parameter strategy.
This objective may include but is not limited to: report cache parameters, expressions, Code-Behind
 
Design a report layout.
This objective may include but is not limited to: selecting report components (crosstab report, TABLIX, design chart), report templates (Report Definition Language), Report Designer, generating report template RDL, adding third-party controls, Visual Studio Report and Development platform
 
Design reports by using Report Builder.
This objective may include but is not limited to: designing a Report Builder model, using Report Builder templates
 
Manage a report environment.
This objective may include but is not limited to: specifying users and roles, subscriptions strategy, designing data sources, designing a deployment plan for completed reports, integrating Microsoft Office SharePoint Server 2007 or Microsoft Windows SharePoint Services 3.0

Designing Data Mining Models (10 percent)

Design a mining model and structure.
This objective may include but is not limited to: assigning a data source, specifying filter properties, reconciling heterogeneous data sources, selecting a refresh strategy (partial or full)
 
Design strategies for staging data for mining.
This objective may include but is not limited to: selecting a method for cleaning data (closed-loop process), specifying partitioning of data into testing and training sets
 
Select a strategy for visualizing data mining results.
This objective may include but is not limited to: DMX queries (drill-through queries, structured and unstructured columns, column aliasing), data mining Microsoft Office Excel add-in, designing a report by using reporting services
 
Select data mining algorithms.
This objective may include but is not limited to: sequence, clustering, time series, neural net, association, classification, decision tree
 
Refine testing models.
This objective may include but is not limited to: applying predictions, analyzing results, performing cross-validation

Administering a BI Solution (15 percent)

Maintain server health.
This objective may include but is not limited to: Resource Governor, performance counters, Analysis Server dynamic management views (DMV)
 
Select a subscription strategy.
This objective may include but is not limited to: data-driven rights management
 
Manage integration services.
This objective may include but is not limited to: loading and scheduling an SSIS package to run, SSIS storage solutions, migration of packages
 
Design a backup strategy.
This objective may include but is not limited to: offsite backup, setting up SSAS backup, Reporting Services backup (databases and keys)
 
Plan and manage upgrades.
This objective may include but is not limited to: data migration, code migration, data security, parallel systems
 
Plan and manage reporting services configuration.
This objective may include but is not limited to: Reporting Services Configuration Manager, SSL management, encryption key management, mail management, Web service interface management, configuration files

Designing the BI Architecture (21 percent)

Design integration between data visualization components.
This objective may include but is not limited to: MOSS, SSRS, SSAS, designing Excel PivotTable reports, application architecture (Microsoft ASP.NET, Web Service, MicrosoftReportViewer Windows Forms control), Windows Mobile, other data visualization tools (Microsoft ProClarity, Microsoft PerformancePoint)
 
Design the data warehouse.
This objective may include but is not limited to: datamarts, integrating legacy data, creating a dimensional model (star schema, snowflake, matrix bridge, conformed dimensions), utilizing upsert in design (TSQL-merge)
 
Plan for scalability.
This objective may include but is not limited to: selecting the appropriate edition of SQL server, selecting the right hardware topology (service distribution), designing the caching strategy (report snapshots, on-demand reports, on-demand-from-cache reports), managing historical data (report history, archiving), selecting the appropriate service host, designing resource utilization of the SSIS package, Read-Only Analysis Server scale-out
 
Plan for performance.
This objective may include but is not limited to: Resource Governor, Proactive Cache, selecting the appropriate storage settings (ROLAP, MOLAP, partition)
 
Manage team development issues.
This objective may include but is not limited to: integrating Microsoft Visual SourceSafe, TFS, VSTS for database pro, selecting BI entry points (SSIS, reporting services), online or offline cube deployment (synchronization)
 
Plan the configuration of a primary data source.
This objective may include but is not limited to: configuring CDC in a server, insert over DML
 
Design a security strategy.
This objective may include but is not limited to: cube, project, reports (security roles, folders, field-level security), encryption keys, SQL services accounts management, encrypted data transmission, SSIS vs. SQL server authentication issues (derail Trojan attacks)

Designing and Deploying SSIS Packages (16 percent)

Design control flow.
This objective may include but is not limited to: tasks and precedence constraints, fuzzy logic control, maintenance tasks, accessing remote data (Web services, FTP), using appropriate variables, data profile
 
Design data flow.
This objective may include but is not limited to: data transformation, blocking transformations (lookup task), extractions (design constraints), designing the load process, creating a package to load facts, creating an SSIS package to load dimension (SCD2), using capture CDC to locate changed data for SSIS
 
Plan the deployment of SSIS packages.
This objective may include but is not limited to: design the structure of packages, troubleshoot and design form problems with package, create a package that reports any errors to a log
 
Design the configuration file for an SSIS package.
This objective may include but is not limited to: enabling configurable values to be consumed by a package
 
Design a logging and auditing strategy.
This objective may include but is not limited to: exception handling, data flow exceptions

Designing an Analysis Services Database (18 percent)

Design cube architecture.
This objective may include but is not limited to: creating and populating a cube, creating KPIs, actions, calculated members, drillthrough, designing SSAS aggregation, creating and manipulating dimensions (Ragged hierarchy, Flexible, Rigid, Semi-additive, Periodicity, Fact Relationships), selecting a processing mode
 
Design for international implementation.
This objective may include but is not limited to: currency conversion, translation, localization
 
Design a data source view.
This objective may include but is not limited to: named queries, denormalization strategy
 
Design perspectives.
This objective may include but is not limited to: enhancing usability through the use of perspectives
 
Design and create business-driven Multidimensional Expressions (MDX) calculations.
This objective may include but is not limited to: calculated measures, creating a named set in MDX
 
Analyze cube performance.
This objective may include but is not limited to: optimizing SSAS aggregation, query cube design

    
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