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Appian ACA100 Exam Questions
- Topic 1: Introduction to the Appian Platform: This topic assesses the understanding of analysts about the key components and capabilities of the Appian Platform. Aspiring Appian analysts will need to demonstrate knowledge of Appian objects, resources, and role-based security. Additionally, the grasp of Appian use cases and user experiences, as well as the ability to differentiate among Appian Designer, Process Modeler, and Cloud Database, will be tested.
- Topic 2: Appian Software Development Lifecycle (SDLC): The topic tests your expertise in navigating the Appian SDLC and your role as an analyst in each phase, from initial build to optimization. You will need to distinguish between different SDLC stages, understand scrum lifecycle elements like DoD and DoR. Your ability to recognize team roles and responsibilities is essential to become certified Appian analyst.
- Topic 3: Requirements Gathering: In this part of the Appian ACA100 exam, your ability to effectively gather and interpret business requirements is evaluated. You will be tested on your understanding of the requirement gathering process, how Appian features can meet business needs, and how to translate requirements into user stories and development plans. Additionally, you will need to address common challenges in this area and know when to apply specific UI components within Appian interfaces.
- Topic 4: User Story Creation: This topic examines your ability to create and manage user stories in an Appian context. To become Appian analyst, you will need to demonstrate how to illustrate standard user story formats, categorize them, and collaborate with Product Owners on managing product backlogs. Additionally, the exam will test skills about identifying story status and understanding the various components of a user story, such as description and acceptance criteria.
- Topic 5: Business Process Modeling: Your skills in business process modeling within Appian will be assessed here. You need to be familiar with different integration types, BPMN elements, and the best practices for defining user roles in process model diagrams. Your ability to specify correct nodes for scenarios, understand gateway types in the Process Modeler, and identify automation opportunities within a process will be critical to become an Appian analyst.
Free Appian ACA100 Exam Actual Questions
Note: Premium Questions for ACA100 were last updated On Jun. 17, 2026 (see below)
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?
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
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?
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.
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?
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
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?
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. Reference: 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. Reference: 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. Reference: Appian Documentation - Managing User Permissions
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?
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.
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