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.

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.

In the Exploration 'Data relationships' view in IBM Cognos Analytics, the thickness of the lines between data items represents the statistical strength of the relationship. Here’s a detailed explanation:

Exploration View: The Exploration view is used to analyze and visualize data relationships in a more interactive manner.

Data Relationships: The lines between data items indicate relationships or correlations between different data points.

Line Thickness: The thickness of these lines visually represents the strength of these relationships. Thicker lines indicate stronger statistical relationships.

Statistical Analysis: This helps users quickly identify significant relationships and patterns in the data, aiding in deeper analysis and better decision-making.