C1000-065 Questions and Answers

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

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).

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

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.

To display HTML report output directly in the body of an emailed report in IBM Cognos Analytics V11.1.x, follow these steps:

Open the Report: Open the report and navigate to the scheduling or bursting options.

Email Delivery Options: In the delivery options pane, configure the email settings.

Display Report in Email Body: Select the option "In the email body, choose 'Display a report'". This allows you to embed the HTML report directly within the email.

Follow Instructions: Follow the dialog box instructions to complete the setup. This ensures the report content is displayed in the email body rather than as an attachment.

Embedding the report in the email body allows recipients to view the report content directly within their email client.

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

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.

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

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.

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.

IBM Cognos Analytics V11.1.x supports integration with collaboration platforms, including Slack. Here’s how this integration works:

Integration Setup: To integrate Cognos with Slack, configuration must be done in both Cognos Analytics and the Slack workspace.

Sharing Reports: Once integrated, users can share links to reports directly to Slack channels from within Cognos Analytics.

Supported Features: The integration allows users to collaborate on report data, discuss insights, and make data-driven decisions within Slack.

Configuration: This involves setting up OAuth tokens and configuring API access to enable seamless communication between Cognos Analytics and Slack.

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.

Jupyter Notebooks Integration: This integration allows developers to use Python and various data science libraries within Cognos Analytics.

Enhanced Insights: Developers can perform advanced analytics and data manipulation using Python, then embed the results directly into Cognos dashboards.

Augmenting Analytics: This capability provides end users with deeper insights and more sophisticated data analysis tools.

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.

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.

Layout Component Reference: This feature in IBM Cognos Analytics allows developers to reference existing report objects within the same report or across different reports.

Reuse for Consistency: By referencing existing components, developers ensure consistency in design and functionality across multiple reports.

Efficiency: This method reduces the effort needed to recreate similar report objects and facilitates easier maintenance and updates.

Reference: According to IBM Cognos Analytics V11.1.x documentation, Layout Component References are intended to streamline report development by allowing the reuse of report objects, thereby enhancing consistency and efficiency.

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.

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.

In IBM Cognos Analytics, the difference between a Story and a Dashboard is primarily in their functionality and presentation:

Dashboard:

Live and Interactive: Dashboards provide real-time, interactive visualizations that allow users to explore and analyze data dynamically.

Widgets: Composed of various widgets (charts, graphs, tables) that update automatically with changes in data.

Story:

Augmented with Animation and Scenes: Stories build on dashboards by incorporating animation and scene changes. They allow users to create a narrative flow with transitions between scenes.

Presentation: Designed to present data in a structured, sequential manner, often used for storytelling and presentations.

A story enhances a dashboard by adding the capability to create a more engaging and informative narrative with animations and scene transitions.