C1000-065 VCE Exam Download

Default Measure Property: Changing the Default measure property in a Crosstab impacts how measures are displayed.

Replacing Existing Measures: If a measure is already set on the Crosstab edges, changing the Default measure will replace the existing measure with the new Default measure.

Crosstab Updates: This change updates the Crosstab intersections to reflect the new Default measure, altering the displayed values accordingly.

Reference: The IBM Cognos Analytics V11.1.x documentation provides comprehensive details on Crosstab properties and the implications of modifying the Default measure, ensuring accurate data representation in reports.

Query Performance: Enhancing query performance involves various optimization techniques.

Avoid Functions in Filters: Using functions in filters can lead to slower query performance because functions often require additional computation and can prevent the database from using indexes efficiently.

Best Practices: It is recommended to simplify filters and avoid complex functions to ensure queries run faster.

Reference: IBM Cognos Analytics V11.1.x documentation suggests avoiding the use of functions in filters as a best practice for improving query performance.

Conditional Block: This feature allows for dynamic content within reports based on user interactions and selections.

Prompt Selections: By using a conditional block, report developers can control the visibility of report objects based on the prompt values selected by the user at runtime.

Use Case: This is useful for creating reports that adapt to user inputs, providing a more tailored and relevant data presentation.

Reference: IBM Cognos Analytics V11.1.x documentation details how to implement conditional blocks to show or hide report objects dynamically based on user inputs.

Understanding Joins: Outer joins and cross joins often result in large intermediate result sets. This can slow down query performance due to the increased data processing required.

Outer Joins: These include rows that do not have matching keys in the joined tables, which means more data to process and potentially more I/O operations.

Cross Joins: These produce Cartesian products of the involved tables. If the tables are large, the resulting dataset can be enormous, leading to significant performance degradation.

Cognos Documentation: The IBM Cognos Analytics V11.1.x documentation advises optimizing join conditions and limiting the use of complex joins such as outer and cross joins to enhance performance.