Appian Appian Certified Analyst ACA100 Exam Dumps: Updated Questions & Answers (October 2025)

For intelligent and automated routing of complex case types based on free text descriptions, integrating with a Natural Language Processing (NLP) system is the most effective solution. NLP can analyze the free text, understand the context, and categorize the cases based on the content, enabling automated and accurate routing.

NLP System Integration Overview:

NLP systems can process and understand human language in text form, identifying key phrases, sentiments, and categories.

By integrating with an NLP service, Appian can automatically route cases to the appropriate department or user based on the analysis of the text provided by external users.

This approach reduces the need for manual review and ensures that cases are handled efficiently and accurately.

Why Not Other Options?:

A. Manual Review Task: This does not automate the process and relies on human intervention, which can be time-consuming and prone to error.

B. Find Functions for Keyword Search: This approach is limited to exact matches and lacks the sophistication needed to understand complex language and context.

C. Translation Service Integration: While useful for language translation, it does not offer the categorization and routing capabilities needed for this use case.

References:

Appian Documentation on NLP Integration: NLP in Appian

Integrating an NLP system with Appian ensures that complex cases are categorized and routed with high accuracy, improving the efficiency and effectiveness of case management.

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According to UI/UX best practices, the button labels in a confirmation dialog should be clear and directly related to the action the user is about to take. "Cancel" allows the user to abort the action, while "Delete" confirms the action to delete the item. This approach ensures that the user understands exactly what will happen when they click either button, reducing the risk of accidental deletion or confusion.

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The "Definition of Done" (DoD) refers to a shared understanding between the Development Team and the Product Owner on the criteria that a user story must meet to be considered complete. This definition helps ensure that all aspects of the user story are fully developed, tested, and meet the agreed-upon standards before being marked as done.

Definition of Done Overview:

DoD includes all conditions that must be satisfied for the work to be accepted by the product owner.

It typically covers code completion, testing, documentation, and any other activities required to deliver a finished product.

Why Not Other Options?:

A. Acceptance Criteria: Defines specific requirements that a user story must meet, but does not encapsulate the broader checklist for completion.

B. Value Statement: Describes the value delivered by a feature, not the criteria for completion.

C. Definition of Ready (DoR): Refers to when a user story is ready to be taken into a sprint, not when it is complete.

References:

Appian Community Success Guide: Definition of Done in Agile Projects

The Definition of Done ensures that the product increment is fully functional, tested, and ready for deployment, maintaining a high standard of quality.

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The organization aims to automate the identification of dissatisfied customers based on the ticket description. To achieve this, leveraging natural language processing (NLP) capabilities is the most efficient method. Appian provides connected systems that allow integration with external NLP services. These services can analyze text data (such as ticket descriptions) to determine the sentiment or classify the text into predefined categories (like "dissatisfied customer").

Natural Language Connected System:

Appian can integrate with third-party NLP platforms such as Google Cloud Natural Language, AWS Comprehend, or Azure Text Analytics via connected systems.

These services analyze the text provided in the ticket description to detect sentiment, keywords, or specific categories indicating dissatisfaction.

Based on the analysis, the system can automatically assign the appropriate team to handle the case.

Why Not Other Options?:

B. Decision Table: While decision tables are useful for rule-based decisions, they are not suitable for interpreting unstructured text like ticket descriptions.

C. Image Analysis Connected System: This option is irrelevant as the task involves text processing, not image analysis.

D. SAIL Form: SAIL forms are primarily used for user interface creation and are not intended for text analysis or classification.

Implementation in Appian:

Create a connected system to integrate with the chosen NLP service.

Configure the NLP service to analyze the text data and return the sentiment or classification results.

Based on the results, use process models to route the ticket to the appropriate team for resolution.

References:

Appian Documentation on Connected Systems: Appian Connected Systems

Appian Community Success Guide:Appian Delivery Methodology

Third-Party NLP Services Integration: Google Cloud NLP Documentation

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Appian is a low-code platform designed for building enterprise applications with a focus on flexibility, user experience, and integration capabilities. The most appropriate use cases for Appian include:

Mobile Compatibility (B):

Appian supports mobile-first design, allowing applications to be easily accessed and used on mobile devices without requiring separate development efforts.

This capability is critical for businesses that need their workforce to access applications on the go.

Multiple Site Tabs for Personas (C):

Appian allows for the creation of different site tabs for different user personas, each with a tailored user experience.

This is ideal for organizations with distinct user groups who need customized interfaces and access to specific parts of the application.

Browser-Based Application (D):

Appian applications are web-based and can be accessed directly from a browser, ensuring easy access and a consistent user experience across different platforms.

Why Not Other Options?:

A. Large-Scale ETL Operations: Appian is not primarily designed for heavy data extraction, transformation, and loading (ETL) tasks, which are typically handled by specialized ETL tools.

E. Embedding in Web Pages: While Appian allows for embedding certain components, it is not the primary focus or strength of the platform compared to full-featured applications.

References:

Appian Documentation on Building Mobile-Ready Applications: Mobile App Development

Appian Community Success Guide: Appian UX Best Practices

These features showcase Appian's flexibility in delivering responsive and personalized user experiences across various devices.

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