Understanding linked input tables

Which of the following statements are true about linked input tables?

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Introduction to AI Apps in Sigma

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This exercise is part of the course

Introduction to AI Apps in Sigma

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Chapter 1 introduces the foundational concepts behind building AI Apps in Sigma, with a focus on how apps integrate user input to drive action-based workflows. A major emphasis is placed on linked input tables, one of the most important building blocks for Sigma apps. You explore how to create linked input tables that display warehouse data alongside user-entered values, enabling direct interaction with underlying datasets. The chapter also covers best practices for governing user input, including controlling which data types can be entered and defining when tables are editable. By the end of this chapter, you’ll have a strong understanding of how to safely and effectively collect user input while maintaining data integrity within your app.

Exercise 1: What are AI Apps?Exercise 2: Exploring a finished app for loan approvals Exercise 3: A finished app for loan officer promotions Exercise 4: Enriching existing data with linked input tables Exercise 5: Understanding linked input tables

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Exercise 6: Creating a linked input table Exercise 7: Capture promotion nominations Exercise 8: Testing linked input table behaviors Exercise 9: Governing linked input table data entry Exercise 10: Managing edits to the loan officers input table

In Chapter 2, you learn how to make your AI apps more interactive and responsive by using modals and action sequences. Modals allow you to present focused pop-up windows within your app, giving users access to additional information or providing a space for more detailed input without cluttering the main interface. You see how modals can improve usability and guide users through specific tasks. The chapter also introduces action sequences, which enable apps to respond dynamically to user actions such as clicking a button or selecting a table row. You learn how to trigger multiple effects from a single interaction, helping automate workflows and streamline user experiences. Together, modals and action sequences give you powerful tools to build apps that feel intuitive, interactive, and purposeful.

Exercise 1: Using modals to capture decisions and user input Exercise 2: Create a modal for capturing promotion nominations Exercise 3: Using action sequences for app workflows Exercise 4: Understanding basic action sequences Exercise 5: Creating an open modal action sequence Exercise 6: Triggering a modal to open and close Exercise 7: Populating modals with control values Exercise 8: Populating a modal with loan officer IDs Exercise 9: Log nominations with an action sequence Exercise 10: Adding conditional action sequences Exercise 11: Preventing duplicate nominations

Chapter 3 focuses on refining the user interface and overall experience of your AI apps. You begin by learning about popovers, a versatile UI element used to hide filter menus or display contextual information only when it’s needed. This helps keep apps clean while still providing users with easy access to controls and details. The chapter also explores a range of design and organizational features that enhance both usability and visual appeal. You learn how to incorporate navigational buttons, dividers and tabbed containers to structure content and guide users through your app more effectively. By the end of Chapter 3, you’ll understand how thoughtful layout and design choices can make your AI apps more intuitive, polished, and enjoyable to use.

Exercise 1: Using popovers to simplify an app's interface Exercise 2: Configure a popover to contain page controls Exercise 3: Leveraging buttons for navigation & interaction Exercise 4: Adding a 'Clear filters' button in a popover Exercise 5: Adding UI & Layout elements for organization Exercise 6: Tidying up the loan officer promotions app Exercise 7: Bringing it all together Exercise 8: Wrap-up