A. the confidence of a match

B. the severity of false positives and false negatives

C. the number of data fields to be used for a particular match job

D. the significance of discriminating value and frequency

A. Ability to import pre-built IBM industry models into Data Architect

B. Ability to create physical data model by reverse-engineering from DB2, Oracle, Sybase, Microsoft SQL Server and Teradata

C. Ability for Business Glossary to automatically use the terms defined in the IDA glossary model

D. Ability to import an Erwin model into Data Architect

A. To bring runtime information about DataStage jobs into the Metadata Repository

B. To bring Business Glossary terms definitions and related assets into the Metadata Repository

C. To bring a blueprint from Blueprint Director into the Metadata Repository

D. To bring FastTrack mapping specifications into the Metadata Repository

E. To bring Cognos BI reports and queries into the Metadata Repository

F. In none of these situations.

A. Microsoft SQL Server

B. Oracle

C. DB2 for Linux, Unix and Windows

D. Informix

A. I, II, III

B. I, II, IV.

C. I, II

D. I, II, III, IV

A. Same operation is performed on each partition of data separately

B. It reduces disk usage for staging areas

C. Data is broken up into each partition

D. It facilitates near-linear scalability

A. The annotation field contains information about errors encountered during validation of a mapping specification.

B. Each transformation rule description field must be filled before a job can be generated.

C. Each transformation rule expression must contain valid SQL syntax.

D. The transformation rule expression field does not need to be filled for each pair of mapped columns before a job can be generated.