C1000-065 Exam Dumps : IBM Cognos Analytics Developer V11.1.x

In IBM Cognos Analytics V11.1.x, dimensional functions should be used in a report's Query calculation when working with a dimensional or dimensionally-modeled data source. Here’s why:

Dimensional Data Sources:

Structure: Dimensional data sources are organized into dimensions, hierarchies, and measures. These structures support advanced analytical capabilities such as drill-down and roll-up.

Dimensional Functions: Functions such asmember,ancestor,children, etc., are specifically designed to navigate and manipulate the hierarchical data structures in dimensional sources.

Query Calculations:

Contextual Calculations: Dimensional functions allow for context-aware calculations, leveraging the inherent structure of the data source. This ensures that calculations respect the dimensional context and hierarchy.

Analytical Depth: Using dimensional functions enables deeper analytical capabilities, such as performing relative date calculations, time series analysis, and hierarchical aggregations.

Dimensional functions are essential for harnessing the full analytical power of dimensionally-modeled data sources.

Drill-through Definition: Drill-throughs in Cognos Analytics allow users to navigate from one report or visualization to another, typically to see more detailed information.

Passing Data Points: Data points can be passed to the target Drill-through provided the data sources have matching values. This ensures continuity and relevance in the data being analyzed.

Targeting Other Dashboards and Reports: Developers can set up Drill-throughs to link to other dashboards or reports, facilitating deeper analysis and better data exploration.

Cognos Documentation: IBM Cognos Analytics V11.1.x documentation provides guidelines on creating and managing Drill-throughs, including passing parameters and targeting various types of reports and dashboards.

Cognos Analytics Data Set: A data set in Cognos Analytics is an extract from a package or data module.

Storage and Refresh: These extracts are compressed, stored locally, and can be refreshed either periodically or manually, depending on the requirements.

Use Case: Data sets are used to improve performance by reducing the need for repeated database queries and enabling offline access to the data.

Reference: IBM Cognos Analytics V11.1.x documentation details the nature of data sets, highlighting their extraction process, storage, and refresh capabilities for enhanced performance and usability.