C1000-065 IBM Cognos Analytics Developer V11.1.x Question and Answers

In IBM Cognos Analytics V11.1.x, the following items can be placed on the opposite edge of a Crosstab in a report:

The Value of a Member:

Dynamic Placement: The value of a member from one edge of the crosstab can be dynamically placed on the opposite edge to compare or analyze data from different perspectives.

Crosstab Intersection:

Intersection Values: Crosstab intersections, where rows and columns meet, can display specific data values, providing detailed insights into the relationships between different data dimensions.

These configurations allow for versatile and detailed data analysis within crosstabs.

In IBM Cognos Analytics V11.1.x, when formatting text in a dashboard crosstab or table, the following statement is true:

Table Summary Row Formatting:

Customization: You can format the table summary row with a different color. This allows for visual differentiation between the summary row and other rows in the table or crosstab.

Implementation: Select the summary row, access the formatting options, and apply the desired color.

Additional Formatting Capabilities:

Column Titles: Although column titles can be formatted individually in terms of font, color, and size, this is a general capability and not specific to summary rows.

Text Wrapping: By default, the object does not wrap text, but this option can be enabled if needed.

Applying specific formatting to the summary row enhances the readability and visual appeal of the data presentation.

To address the user’s requirement to see only newer entries based on the 'Order Date' column while keeping historical data available in IBM Cognos Analytics V11.1.x, you should apply a filter to the "Order Date" attribute. Here’s how:

Open Data Module: Access the data module containing the table.

Apply Filter:

Filter Creation: Select the "Order Date" column and create a filter. Specify the criteria to include only newer entries, such as dates within the last year or a specific date range.

Save Filter: Save the filter to ensure it applies whenever the table is used in new reports.

Usage:

Report Design: When designing new reports, the filter will automatically apply, showing only the newer data as specified.

Applying a filter on the "Order Date" attribute allows for dynamic control over the data displayed without modifying the original table or creating duplicates.

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.

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

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

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

ï‚·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.

When designing Dimensionally Modeled Relational (DMR) structures in Framework Manager, understanding how determinants influence hierarchy levels is crucial. Here is an explanation:

Determinants in Framework Manager: Determinants are used to define the granularity of data and how it is grouped. They are essential in DMR models to ensure correct aggregation and drill behavior.

Hierarchy Levels: The order in which determinants are defined in a query subject influences the hierarchy levels. Higher-order determinants represent higher levels in the hierarchy, while lower-order determinants represent more granular levels.

Configuration: Configure determinants in the properties of a query subject. The sequence of determinants determines how data is aggregated and navigated in reports.

Impact on OLAP Behavior: Properly ordered determinants ensure that measures and dimensions behave correctly, allowing users to drill down and roll up data accurately in reports.

Dynamic Query Analyzer: This tool is used to analyze and troubleshoot queries in IBM Cognos Analytics.

Graphical Representations: The Dynamic Query Analyzer provides graphical representations of the query logs produced by Dynamic Query Mode (DQM) queries. This visualization helps in understanding the query execution and performance.

Troubleshooting Assistance: By analyzing these graphical logs, developers can identify bottlenecks, optimize query performance, and resolve issues more efficiently.

ï‚·Query Cache: The query cache in IBM Cognos Analytics helps to store the results of frequently executed queries to improve performance.

ï‚·Avoiding Roundtrips: When the same master-detail request is executed repeatedly, the query cache can serve the results from the cache instead of making another roundtrip to the database.

ï‚·Performance Improvement: This reduces the load on the database and speeds up the report generation process by utilizing the cached data.

ï‚·Reference: IBM Cognos Analytics V11.1.x documentation details the functionality of the query cache, including its ability to handle repetitive master-detail requests efficiently by avoiding unnecessary database roundtrips.

ï‚·Local Filters: These allow developers to filter data at the visualization level, providing flexibility in data presentation without altering the underlying data source.

ï‚·Keep/Exclude Data Points: This feature enables developers to interactively refine the data displayed in a visualization by keeping or excluding specific data points directly within the dashboard.

ï‚·Cognos Documentation: The IBM Cognos Analytics V11.1.x documentation provides detailed instructions on creating local filters and using interactive filtering options to enhance the user experience in dashboards.

Enabling the local cache property on a query in IBM Cognos Analytics V11.1.x has the following implications:

Improved Performance:

Data Caching: When local cache is enabled, data is cached locally, which can improve performance by reducing the need to repeatedly fetch data from the source.

Efficiency: This caching mechanism speeds up report execution and interaction times, especially when dealing with large datasets or complex queries.

Batch Reports:

Support: Local caching is particularly beneficial for batch reports, where multiple runs can leverage the cached data to enhance performance.

Impact on Scalability:

Scalability Considerations: While caching can improve performance, it might impact the scalability of the report depending on the volume of data and the frequency of cache refreshes.

Enabling local cache optimizes report execution by leveraging previously fetched data, thus enhancing user experience.

ï‚·Filter Expression: The filter expression[Product Group] = # sq( prompt( 'productgroups', 'token', 'Clothes' ) )#uses a prompt macro.

ï‚·Default Text: The string 'Clothes' acts as the default value for the prompt. If the user does not provide a value at runtime, 'Clothes' will be used in the generated query.

ï‚·Macro Function: Thepromptfunction retrieves the value provided by the user or defaults to 'Clothes' if no value is supplied.

ï‚·Reference: IBM Cognos Analytics V11.1.x documentation provides detailed information on using macros and prompt functions, including the use of default values in filter expressions.

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.

ï‚·Joined Views in Data Modules: When creating joined views in Cognos Analytics, the order of joins and evaluation is significant.

ï‚·Execution Logic: The joined view of tables (a and b) is first evaluated. The result of this join is then used to perform the subsequent join with table (c).

ï‚·Post-join Evaluation: This approach ensures that the initial join logic is preserved and applied before the next join is executed.

ï‚·Reference: The IBM Cognos Analytics V11.1.x documentation outlines the process of creating and using joined views, detailing how the execution logic is handled by the database.

ï‚·Layout Positioning for Mobile Devices: When designing dashboards for different mobile devices, the layout positioning must be flexible.

ï‚·Relative Positioning: This option ensures that dashboard components are arranged in a way that adapts to different screen sizes and orientations, providing a consistent user experience across various devices.

ï‚·Adaptive Layouts: Relative positioning allows the dashboard elements to resize and reposition automatically based on the device, ensuring usability and accessibility.

ï‚·Reference: IBM Cognos Analytics V11.1.x documentation recommends using relative positioning for dashboards intended for mobile devices to maintain optimal presentation and functionality.