Describe IBM Cognos Analytics and its position within an analytics solution
Describe IBM Cognos Analytics components
Describe IBM Cognos Analytics at a high level
Explain how to extend IBM Cognos

Define the role of a metadata model in Cognos Analytics
Distinguish the characteristics of common data structures
Understand the relative merits of each model type
Examine relationships and cardinality
Identify different data traps
Identify data access strategies

Examine key modeling recommendations
Define reporting requirements
Explore data sources to identify data access strategies
Identify the advantages of modeling metadata as a star schema
Model in layers

Follow the IBM Cognos and Framework Manager workflow processes
Define a project and its structure
Describe the Framework Manager environment
Create a baseline project
Enhance the model with additional metadata

Verify relationships and query item properties
Create efficient filters by configuring prompt properties

Describe multi-fact queries and when full outer joins are appropriate
Describe how IBM Cognos uses cardinality
Identify reporting traps
Use tools to analyze the model

Understand the benefits of using model query subjects
Use aliases to avoid ambiguous joins
Merge query subjects to create as view behavior
Resolve a recursive relationship
Create a complex relationship expression

Create virtual dimensions to resolve fact-to-fact joins
Create a consolidated modeling layer for presentation purposes
Consolidate snowflake dimensions with model query subjects
Simplify facts by hiding unnecessary codes

Use calculations to create commonly-needed query items for authors
Use static filters to reduce the data returned
Use macros and parameters in calculations and filters to dynamically control the data returned

Make time-based queries simple to author by implementing a time dimension
Resolve confusion caused by multiple relationships between a time dimension and another table

Use determinants to specify multiple levels of granularity and prevent double-counting

Identify the dimensions associated with a fact table
Identify conformed vs. non-conformed dimensions
Create star schema groupings to provide authors with logical groupings of query subjects
Rapidly create a model using the Model Design Accelerator
Rapidly create a model using the Model Design Accelerator

Identify the effects of modifying query subjects on generated SQL
Specify two types of stored procedure query subjects
Use prompt values to accept user input

Examine the IBM Cognos security environment
Restrict access to packages
Create and apply security filters
Restrict access to objects in the model

Apply dimensional information to relational metadata to enable OLAP-style queries
Sort members for presentation and predictability
Define members and member unique names
Identify changes that impact a MUN

Connect to an OLAP data source (cube) in a Framework Manager project
Publish an OLAP model
Publish a model with multiple OLAP data sources
Publish a model with an OLAP data source and a relational data source

Governors that affect SQL generation
Stitch query SQL
Conformed and non-conformed dimensions in generated SQL
Multi-fact/multi-grain stitch query SQL
Variances in IBM Cognos Analytics - Reporting generated SQL
Dimensionally modeled relational SQL generation
Cross join SQL
Various results sets for multi-fact queries

Identify environment and model session parameters
Leverage session, model, and custom parameters
Create prompt macros
Leverage macro functions associated with security

Perform basic maintenance and management on a model
Remap metadata to another source
Import and link a second data source
Run scripts to automate or update a model
Create a model report

Identify how minimized SQL affects model performance
Use governors to set limits on query execution
Identify the impact of rollup processing on aggregation
Apply design mode filters
Limit the number of data source connections
Use the quality of service indicator

Segment and link a project
Branch a project and merge results

Specify package languages and function sets
Control model versioning
Nest packages

Appendix A. Additional modeling techniques

Leverage a user defined function
Identify the purpose of query sets
Use source control to manage Framework Manager files

Appendix B. Modeling multilingual metadata

Customize metadata for a multilingual audience