SYSTEM ARCHITECTURE CONCEPTS - 14%
Candidates should be able to identify the purposes of different types of metadata, the characteristics of conceptual modeling, the uses and benefits of logical and physical models, strategies for managing system expansion and contraction, the components of an analytic ecosystem and given a scenario, identify the logical model that should be created, how to use an extended logical data model, the strategies to extend traditional application deployment and how to architect a non-production environment.
INFORMATION MANAGEMENT AND DATA GOVERNANCE - 11%
Candidates should be able to identify the benefits of effective master data management and given a scenario, identify which loading strategy should be used, which Teradata data acquisition tool(s) should be used, how to create data for a non-production environment, and the data retention, placement, and archive strategies that should be used.
PERFORMANCE DESIGN - 16%
Candidates should be able to, given a scenario, identify physical design choices for indexes, join indexes, row partitioning, column partitioning, time series data, data types, which statistics should be collected and how often for optimal performance and identify the compression options that should be used along with identifying how to manage tables and their relationship to the MAPS feature.
ARCHITECTING FOR SYSTEM PERFORMANCE - 5%
Candidates should be able to identify the benefits of Teradata Virtual Storage and Teradata Intelligent Memory and given a scenario, identify the Unity components and strategy that should be used.
DATA INTEGRATION - 11%
Candidates should be able to, given a scenario, identify appropriate data transformation strategy(ies), the appropriate method level to organize tightly, loosely and non-coupled data, when and how surrogate keys should be generated or when natural keys should be used, the strategy that should be used to achieve the correct level of data granularity, the design considerations when using complex data types, when integrating temporal data and when integrating geospatial data and including a data model method, identify the benefits and tradeoffs.