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

To combine several images and text objects into one object in a dashboard, follow these steps to group the objects:

Select Objects: In the dashboard editor, select the images and text objects that you want to combine.

Group Objects: Use the "Group" option available in the toolbar or right-click menu to group the selected objects. This creates a single group object that can be moved and aligned as a unit.

Adjust Group Properties: You can adjust the properties of the group as needed, such as alignment, size, and position.

Grouping objects simplifies the management of multiple items, making it easier to move and align them within the dashboard layout.

Render Variable: This is used to conditionally show or hide objects within a report based on the value of a variable.

Application: By setting a render variable, you can control the display of the list object based on certain conditions. If the condition evaluates to true, the object will be shown; if false, it will be hidden.

Cognos Documentation: The IBM Cognos Analytics V11.1.x documentation highlights the use of render variables to manage report object visibility without affecting the data processing logic.

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.