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

To modify a prompt in a relational sourced report to support multi-select functionality, make the following changes:

Change Prompt Property Usage to Select-many:

Edit the Prompt: Select the prompt object in the report.

Property Panel: In the properties panel, change the "Usage" property from "Select-one" to "Select-many". This allows users to select multiple values in the prompt.

Update Filter Expression:

Edit Query: Open the query associated with the prompt.

Change Filter Expression: Modify the filter expression from using "=" to "in". This change ensures that the filter can handle multiple values selected by the user.

These modifications enable the prompt to accept multiple selections and apply the filter accordingly in the report.

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.

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.