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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Business Process Modeling | 20% | - Appian Process Modeler best practices - BPMN 2.0 elements and notation - Designing for maintainability and scalability - Swimlanes, events, gateways, sub-processes |
| Topic 2: Introduction to the Appian Platform | 24% | - Environments, deployment, governance - Low-code architecture and core concepts - Design objects: Records, Sites, Reports, Interfaces - Agile delivery and application lifecycle |
| Topic 3: User Story Creation | 14% | - Aligning stories to Appian components - Persona-driven design and user experience - INVEST criteria and acceptance criteria - Story splitting and prioritization |
| Topic 4: Requirements Gathering | 20% | - Business rules vs process logic - Identifying automation opportunities - Defining KPIs, SLAs, and Appian-ready specifications - Stakeholder engagement and RACI matrices |
| Topic 5: Software Development Lifecycle on Appian | 22% | - Role of analyst in each phase - Planning, analysis, design, build, test, deploy - Change management and version control - Testing strategies and UAT preparation |
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23. Frage
Which three use cases are best suited for implementation with Appian?
Antwort: B,C,D
Begründung:
A use case that requires integrations with different systems (A): Appian is designed to integrate seamlessly with various external systems, making it suitable for use cases that involve data exchange or communication between multiple systems. This capability allows for the creation of comprehensive and connected business processes within a single platform.Appian Integration Capabilities
A use case that requires multiple approval workflows (C): Appian excels in automating and managing complex business workflows, including those that require multiple layers of approval. Its workflow engine supports the orchestration of tasks, ensuring that the right people are involved in the process at the right time.Appian Workflow Automation
A use case that requires reporting and displaying graphs (E): Appian provides robust reporting and analytics capabilities, allowing users to create detailed reports and visualizations. This makes it an ideal choice for use cases where data needs to be reported and visualized effectively.Appian Reporting and Analytics
24. Frage
The HR management team wants to aggregate data to show the number of employees across regions and to be able to drill down into the data.
Which three user story requirements should be collected to assist the development team?
Antwort: A,C,D
Begründung:
Data Source (A): The first step in building any report is identifying the source of the data. Understanding where the data comes from is crucial because it affects how the data will be queried, filtered, and displayed. It also impacts performance, security, and accuracy of the data. Appian reports can pull data from various sources such as Appian databases, external databases, or even from integrations with other systems. Documenting this information allows the development team to connect the report to the correct data source, ensuring that the report reflects accurate and up-to-date information.Appian Documentation - Data Sources
Report Type (B): It is essential to define the type of report required. In this scenario, the HR management team wants an aggregate view with drill-down capabilities. The report type will determine how the data is visualized, whether it is a pie chart, bar graph, or tabular format. This user story requirement ensures that the developers design a report that meets the HR team's needs and expectations for viewing and interacting with the data.Appian Documentation - Creating Reports
Role-Based Permissions (D): Role-based permissions are critical for ensuring that users see only the data they are authorized to access. For instance, while an HR executive might have access to all regions' data, a regional manager might only see data for their specific region. Defining these permissions upfront is vital for security and compliance. The development team will use this information to implement the correct access controls in the report, which is crucial for protecting sensitive employee information.Appian Documentation - Managing User Permissions
25. Frage
An organization wants to automate identification of its dissatisfied customers based on the ticket description and assign the appropriate team to provide a quick resolution.
What is the best way to auto-classify the dissatisfied customers as part of processing?
Antwort: B
Begründung:
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
26. Frage
The order management team receives orders via different channels, including email. Emails are accompanied by attachments containing the details of the order information. The team reviews the emails and attachments for clarity. Then, they extract the order specifics to create a customer record in their Customer Relationship Management (CRM) platform and an order in their Order Management System (OMS).
What is the most effective form of automation to help increase fulfillment efficiency in this process?
Antwort: C
Begründung:
The most effective form of automation to increase fulfillment efficiency in this process is using trained artificial intelligence (AI) to extract order data information from the emails and attachments. AI can be trained to recognize and extract key details such as customer names, order numbers, and product information directly from the content of the emails and attachments. This automation significantly reduces the manual effort required to review and input data, thereby speeding up the order processing time and reducing the chance of errors.Appian Documentation - AI and Automation Capabilities Note: The other options (B, C, D) provide minimal automation and would still require significant manual intervention, which does not effectively enhance fulfillment efficiency.
27. Frage
A team of employees manages invoice processing and payments for their organization.
Each day, the team receives invoices via email. The team reviews the invoices, sends payments, and logs the payments manually in a shared spreadsheet.
Where is the best opportunity for automation in this manual process?
Antwort: C
Begründung:
The best opportunity for automation in the manual process described is to leverage Optical Character Recognition (OCR) and workflow tools. OCR can automatically extract data from the invoices received via email, and the workflow tools can automate the review process, checking the data for accuracy, processing payments, and prompting employees only when exceptions are found.
OCR and Workflow Tools Overview:
OCR technology converts the text in scanned documents or images into machine-readable data, which can be automatically processed.
Workflow tools in Appian can then route the extracted data through a series of steps, automating tasks like data entry, validation, and payment processing.
Automating these steps reduces manual effort, minimizes errors, and ensures that invoices are processed more efficiently.
Why Not Other Options?:
A . Robotic Process Automation (RPA) for thank you messages: This is a less critical part of the process and does not significantly reduce manual work.
B . Creating a task for manual input: This still involves manual data entry, which does not fully utilize automation potential.
C . Macro in the shared spreadsheet: This provides minimal automation and doesn't address the root of the problem, which is manual data entry.
References:
Appian Documentation on OCR Integration: OCR in Appian
Appian Workflow Automation Guide: Automating Processes in Appian
By using OCR and workflow tools, the organization can significantly reduce manual processing time and increase the accuracy of invoice handling.
28. Frage
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