UiPath-ASAPv1 Practice Exam Questions with Answers UiPath Automation Solution Architect Professional v1.0 Exam Certification

This answer is correct because the Object Repository allows for creating and reusing UI taxonomies inside and across automation projects. By referencing Library B, which contains the Object Repository for application C, process A can reuse the UI elements defined there without creating a new Object Repository or using selectors. This way, process A can benefit from the centralized management, reusability, and reliability of the UI elements in the Object Repository12

References:

• Studio - About Object Repository - UiPath Documentation Portal
• Object Repository | UiPath UI Library | UiPath Academy

 According to the UiPath documentation1, Automation Ops is a component of the UiPath platform meant for creating, deploying, and managing Robotic Process Automation (RPA) governance policies in UiPath Studio, UiPath StudioX, and UiPath Assistant. You can use a single, simple web portal to manage and implement governance rules based on user profiles, such as defining the acceptable feeds, packages, widgets, and activities, restricting the applications and URLs that can be automated, and assigning roles and responsibilities for reviewing and approving the automation projects. Automation Ops also integrates with Git repositories and CI/CD pipelines to provide source control and automation delivery capabilities. Therefore, the correct answer is D. Configure and deploy governance policies, as this is one of the main functionalities of Automation Ops. References: 1: Automation Ops - UiPath Documentation

The key difference between Attended and Unattended Automations in UiPath is the level of human involvement and interaction required for the automation to run. Attended Automations are designed to assist human users with their tasks, such as filling forms, validating data, or providing guidance. They are triggered by the user and run on the user’s machine, usually in the foreground. They are best suited for smaller, fragmented tasks that need human input or decision making. Unattended Automations are designed to perform complex and repetitive tasks without human intervention, such as processing invoices, generating reports, or updating databases. They are triggered by a schedule, an event, or an API call and run on dedicated machines, usually in the background. They are best suited for large-scale, batch-mode tasks that need high reliability and scalability.

References:

• Overview - Attended Vs. Unattended Automation
• Attended Or Unattended RPA? Advantages for Both Solutions
• Robot - Attended Vs Unattended Robots

In UiPath Orchestrator, using the queue name as an argument for the process is the most efficient way to handle varying data sources for different schedule times. When scheduling the process, you can specify different queue names as input arguments for each trigger (9AM, 1PM, 6PM). This approach allows for a single, flexible process that adapts to different data sources at different times without the need for multiple deployments or manual updates to assets or configuration files.

References:

• UiPath Orchestrator Guide: Managing Triggers
• UiPath Orchestrator Guide: About Arguments

Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and perform tasks that are difficult or impossible to program explicitly. ML models can be trained to recognize patterns, make predictions, and generate outputs based on the input data. ML models can be integrated into automation solutions using UiPath AI Center, a cloud-based platform that allows users to deploy, manage, and consume ML models in their automations.

Among the four business scenarios given, the one that is best automated using ML is to classify emails in appropriate categories based on their subject and body. This is because email classification is a natural language processing (NLP) task that requires understanding the meaning and context of the text, which is not easy to achieve with rule-based or deterministic approaches. ML models can be trained to learn from a large corpus of labeled emails and assign categories to new emails based on their similarity and relevance. This can help automate the email management process and improve the efficiency and accuracy of email handling.

The other three scenarios can be automated using Robotic Process Automation (RPA), which is a technology that mimics human actions to interact with applications and systems. RPA can be used to create support tickets inside a helpdesk platform, calculate hotels inside a spreadsheet, and migrate data from one database to another, by following predefined steps and rules. These scenarios do not require ML models, as they do not involve complex or ambiguous data or tasks.

References:

• RPA & AI Integration with AI Center | UiPath
• Become an Automation Solution Architect | UiPath Academy
• RPA Solution Architect - UiPath Academy

The main purpose of Workflow Analyzer in UiPath Studio is to evaluate the usage of best practices within the development process, identify design errors, and pinpoint areas for improvement. Workflow Analyzer is a static code analysis tool that scans automation projects for potential issues, inefficiencies, and deviations from best practices. It provides developers with actionable feedback and recommendations, helping to improve the quality, maintainability, and performance of the workflows. This tool is instrumental in ensuring that automations are built to the highest standards, aligning with organizational guidelines and industry best practices.

References:

• UiPath Studio Guide: Workflow Analyzer
• UiPath Best Practices: Workflow Design

 UiPath Solution Accelerators are automation frameworks that are custom-built for specific use cases. They use best practices and pre-built components that can be modified, expanded, and customized based on the needs of each specific use case and customer2. To get started with a UiPath Solution Accelerator, the recommended steps are:

• Review the Documentation & Deployment Guide: This guide provides an overview of the solution accelerator, its architecture, dependencies, and configuration. It also explains how to deploy the solution accelerator to your environment and test it1.
• Familiarize yourself with the project(s): Each solution accelerator consists of one or more UiPath projects that implement the automation logic. You can open these projects in UiPath Studio and explore their workflows, activities, variables, and arguments1.
• Set up and adjust the Orchestrator process folder, dependencies, assets & queues, libraries and projects: To run the solution accelerator, you need to set up a process folder in UiPath Orchestrator and configure its dependencies, assets, and queues. You also need to publish the libraries and projects from UiPath Studio to Orchestrator and assign them to the process folder1.
• Make adjustments as needed: Depending on your specific use case and customer requirements, you may need to customize the solution accelerator by adding, removing, or modifying its components. For example, you may need to change the input or output data formats, integrate with different applications, or enhance the exception handling logic1.

References:

• 1: Download and Deploy a Solution Accelerator - academy.uipath.com
• 2: How Solution Accelerators fast-track AI initiatives | UiPath

UiPath Communications Mining is a platform that uses AI to monitor and automate business communications across different channels, such as email, chat, calls, and tickets. It can extract insights from unstructured messages, such as the reasons for contact, the data fields, and the sentiment. UiPath Document Understanding is a framework that uses AI to process and analyze documents of various formats, such as PDF, Word, Excel, and images. It can obtain key information from attached files, such as the document type, the fields, and the values. By combining Communications Mining and Document Understanding, businesses can optimize data extraction and analysis in an automated business process. For example, a customer service process that involves reading both messages and documents to complete a request can be automated by using Communications Mining to understand the customer’s intent and data from the message, and Document Understanding to extract the relevant information from the document. This way, the process can be faster, more accurate, and more scalable. References:

• Communications Mining - Overview - UiPath Documentation Portal
• [Document Understanding - Overview - UiPath Documentation Portal]
• How and where Communications Mining can be deployed

When UiPath Robots are deployed in Service Mode, they operate as a Windows service. This configuration allows the Robot to execute jobs assigned via Orchestrator independently of any user session on the machine. The Robot in Service Mode does not require a user to be logged in to the machine to start and execute processes, enabling unattended automation scenarios. This capability is particularly useful for automations that need to run at scheduled times or be triggered by specific events without human intervention.

References:

• UiPath Robot Guide: Robot Service Modes
• UiPath Orchestrator Guide: About Jobs

UiPath Automation Hub documentation discusses the various stages of the automation lifecycle, including the Decision Pipeline step. This step is crucial for evaluating and deciding which automation ideas will proceed to the implementation phase based on their potential impact, feasibility, and alignment with business goals. This ensures that resources are allocated to the most valuable and viable automation projects. (Source: https://docs.uipath.com/automation-hub/docs/idea-lifecycle )

 A reusable component is a workflow that can be compiled and packaged as an activity and reused in multiple processes. A reusable component should be designed with a clear purpose, input and output parameters, and error handling. A reusable component should also follow the best practices of modularity, reusability, maintainability, and scalability. A reusable component can be categorized into three layers: User Interface interaction layer, Logic layer, and Persistence layer. The User Interface interaction layer is responsible for interacting with applications, such as clicking, typing, or scraping data. The Logic layer is responsible for implementing the business logic, such as calculations, validations, or decision making. The Persistence layer is responsible for saving or retrieving data, such as using databases, files, or queues. These three layers are essential for designing reusable components, as they represent the common functionalities that can be shared across different processes. However, process steps that are not part of these three layers are redundant for a Solution Architect to take into account when designing the reusable components for a project. Process steps that are not part of these three layers are specific to a particular process or scenario, and they may not be applicable or compatible with other processes. For example, a process step that involves sending an email notification to a specific recipient or group may not be relevant or suitable for another process that uses the same reusable component. Therefore, such process steps should be avoided or separated from the reusable components, and instead be implemented in the main workflow of the process that requires them. References:

• 18.3 - Library (Reusable components) - UiPath Community Forum
• How to make useful Libraries (Reusable components) - UiPath Community Forum
• Reusable Components - UiPath Documentation Portal

WD8 is a process performer that executes the automation workflow for each transaction item from Queue 2. It uses the data extracted from the documents and the processing output to perform the required actions on the target applications. It also handles any exceptions that may occur during the execution and updates the status of the transaction items accordingly.

References:

• About Queues and Transactions
• About the REFramework
• Document Understanding - Introduction

The primary purpose of UiPath Data Service is to provide a persistent data storage service that brings powerful no-code data modeling and storage capabilities to your Robotic Process Automation (RPA) projects. UiPath Data Service allows you to securely store and manage your business data within UiPath, and use it for various automation scenarios, such as aggregating data from multiple systems, supporting long-running business processes, and passing data across attended and unattended workflows. UiPath Data Service is integrated with Studio and Robot, and offers rich relational data types, integrated security, and instantaneous and adjustable provisioning and deployments. (https://docs.uipath.com/data-service/automation-cloud/latest/user-guide/introduction)

References:

• Data Service - Introduction - UiPath Documentation Portal
• Data Service - UiPath Documentation Portal
• Data Visualization in UiPath Data Service | Community Blog
• UiPath Data Service: Data management and storage simplified for your automation

In UiPath Data Service, an "Entity" represents a data model that defines the structure of data stored within the service, similar to a table in a relational database. Entities are used to model and manage business data in Data Service.

This answer is correct because the one-job-per-file approach is a recommended architecture for Document Understanding processes that allows for processing each document as a separate job, regardless of the source or the trigger. This approach has several benefits, such as:

• It simplifies the development, debugging, and deployment of the DU Process, as it does not require any looping logic or complex exception handling.
• It enables the use of Orchestrator queues to store and manage the documents to be processed, which provides better visibility, tracking, and reporting of the process status and performance.
• It reduces the license consumption and re-execution costs, as each document is processed only once and any errors or exceptions are handled at the individual document level, without affecting the rest of the documents.
• It supports both attended and unattended scenarios, as well as human-in-the-loop validation via Action Center, using the same workflows and logic.
• It allows for easy scaling and load balancing of the process, as multiple robots can process documents in parallel from the same queue, without any conflicts or dependencies12

References:

• RPA Framework for Document Understanding - UiPath Community Forum
• The Business Process for Document Understanding - UiPath Studio Template Explained

Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. Unlike traditional programming, where a developer writes the instructions to solve a problem, ML algorithms learn from the data. They improve their performance as the amount of data increases. This learning process involves training an ML model on a dataset, allowing it to learn from the examples provided. Once the model is trained, it can make predictions or decisions based on new, unseen data. This approach is powerful in handling complex tasks where defining explicit rules is challenging.

References:

• UiPath AI Center Guide: Introduction to Machine Learning
• UiPath AI Fabric: Machine Learning Models

The rule-based extraction and the model-based extraction are two different methods of data extraction that target different types of documents. The rule-based extraction is suitable for structured documents that have a fixed format and layout, such as forms, tax returns, or certificates. This method relies on rules (such as regular expressions) and templates (such as position or occurrence patterns) to identify and extract the data of interest from the document. The rule-based extraction ensures high accuracy and speed for already known documents, but it requires manual configuration and maintenance of the rules and templates, and it cannot handle variations or changes in the document format. The model-based extraction is suitable for semi-structured documents that have varying formats and layouts, but contain similar types of information, such as invoices, receipts, or purchase orders. This method relies on pre-trained models (such as machine learning or artificial intelligence models) or custom models (such as user-defined models) to analyze and extract the data of interest from the document. The model-based extraction can handle variations and changes in the document format, and it can learn from feedback and improve over time, but it requires training data and validation, and it may not achieve the same level of accuracy and speed as the rule-based extraction for some documents. References:

• Data Extraction Overview - UiPath Document Understanding
• Document Processing with Improved Data Extraction | UiPath
• Document Understanding - Machine Learning Extractor - UiPath

Rule-based extraction is a technique that applies a set of predefined rules and patterns to extract data from a document. For example, you can use document templates, data position, occurrence patterns, or regular expressions to define the rules. Rule-based extraction is suitable for structured documents, such as forms, that have a fixed format and layout. However, rule-based extraction can be limited by the complexity and variability of the rules, and it may not be able to handle semi-structured or unstructured documents, such as invoices, contracts, or emails, that have different formats, layouts, or data types.

Model-based extraction is a technique that uses machine learning algorithms to automatically learn and extract data from a document. For example, you can use classification, clustering, or regression algorithms to train a model based on a set of labeled or unlabeled data. Model-based extraction is effective for semi-structured or unstructured documents, such as invoices, contracts, or emails, that have similar types of information but different formats, layouts, or data types. However, model-based extraction can require more computational resources and data preparation, and it may not be as accurate or consistent as rule-based extraction.

References:

• Document Processing with Improved Data Extraction | UiPath
• Data Extraction Types & Techniques: A Complete Guide
• NLP Methods’ Information Extraction for Textual Data: An Analytical …

An Orchestration Process is a type of long-running workflow that supports service orchestration, human intervention, and long-running transactions in unattended environments1. An Orchestration Process can use persistence points, such as Wait for Queue Item and Resume or Wait for Job and Resume, to suspend and resume the execution based on certain conditions1. There is no limit on the number of jobs or other triggers that a job can wait for at a time, as long as there are enough runtimes available on the host machine2. However, if the number of pending jobs exceeds 10,000, a warning message is displayed in Orchestrator3.

References: 1: Studio - Orchestration Process 2: Orchestrator - About Jobs 3: Maximum number of pending jobs - Orchestrator

UiPath Unassisted Task Mining is a cloud-based service that collects and analyzes employee desktop data to identify tasks with high automation potential. The Admin Portal and Analyzer are web applications that are used to manage and review the Unassisted Task Mining projects. They are delivered as part of the UiPath Automation Cloud, which is built and hosted on Microsoft Azure. Microsoft Azure is a cloud computing platform that provides various services and solutions for building, deploying, and managing applications and data. UiPath Automation Cloud leverages Microsoft Azure’s security, scalability, and reliability features to offer a seamless and secure user experience. (https://docs.uipath.com/task-mining/automation-cloud/latest/user-guide/unassisted-task-minining-introduction)

References:

• Unassisted Task Mining - Introduction - UiPath Documentation Portal
• UiPath Automation Cloud - Introduction - UiPath Documentation Portal
• Microsoft Azure - Overview

To access Automation Cloud, you do not need a dedicated infrastructure, an on-premises installation, or an offline access. You only need an internet connection with TLS and a compatible web browser. Automation Cloud is a cloud-based platform that delivers the full UiPath Platform, including SaaS robots and self-hosted robots, in under a minute, from any of six regions around the world. You can access it from any supported browser, such as Google Chrome, Microsoft Edge Chromium, Mozilla Firefox, or Apple Safari. Other browsers that are not listed, older versions of the listed ones, or mobile browsers are not supported and may cause errors or malfunctions. References: Automation Cloud - Software requirements - UiPath Documentation Portal, Complete cloud-based enterprise automation solution | UiPath

The single-node deployment option is recommended for evaluation and demo scenarios because it is simpler and faster to set up and requires less resources. It involves installing the Automation Suite on a single server node, which hosts both the cluster management services and the UiPath products and shared components. This option is not suitable for production use, as it does not provide high availability, scalability, or fault tolerance. It also does not support special workloads that require GPU capabilities, such as Task Mining analysis and Document Understanding pipelines. References:

• Automation Suite - Deployment architecture - UiPath
• Automation Suite - Supported use cases for single-node and multi-node installations - UiPath

The process shown in the diagram can be optimized by reading the email as the first step and opening CRM and logging in only if there’s an email. This way, the process can avoid unnecessary steps and save time and resources. If there is no email, the process can end without opening CRM. If there is an email, the process can extract the user information from the email and use it to log in to CRM and navigate to the user profile. This can also reduce the risk of errors and exceptions due to invalid or missing user information. Reading the email as the first step can also help the process to prioritize the emails based on their importance or urgency and handle them accordingly. References:

• UiPath Process Mining: Use process monitoring and optimization to propel RPA
• Optimizing Processes with UiPath Process Mining
• Optimization maximum utilisation - UiPath Community Forum

Long-running workflows are processes that require logical fragmentation or human intervention (validations, approvals, exception handling) such as invoice processing and performance reviews. UiPath provides a dedicated project template in Studio called Orchestration Process, which enables the use of a set of activities to handle these scenarios. The Orchestration Process template allows the workflow to be suspended and resumed based on certain conditions, such as the completion of a job, a queue item, a form task, or an external task. The template also integrates with UiPath Action Center, which is a feature that enables human intervention in long-running unattended workflows. The Orchestration Process template is the recommended option for designing and developing long-running workflows in UiPath Studio. References:

• Orchestrator - Working With Long-running Workflows
• New Component - Long Running Workflow Template with UiPath Tasks
• Long Running Workflow Template for Task Creation - UiPath Marketplace

A development Specification Document (DSD) is a critical document for a smooth implementation of an RPA project. It should contain the automated process details and focus on two main categories: runtime guide and development details1. The runtime guide should provide information about how the robot will run, such as the input and output files, the configuration settings, the error handling, the logging and reporting, the credential management, and the Orchestrator usage. The development details should provide information about how the robot was built, such as the packages, the environment, the source code repository, the workflow components, the reusable components, the invoke tree, the custom logs, and the background versus foreground automation.

References: 1: Studio - Automation Lifecycle - UiPath Documentation Portal

This answer is correct because high runtime, high volume, and low SLA indicate that the automation process is complex, frequent, and critical, which requires more resources and licenses to execute and manage. For example, more unattended robots may be needed to run the process without human intervention, more Orchestrator tenants may be needed to isolate and scale the process across different environments, and more Studio licenses may be needed to design and test the process12

References:

• Automation Pricing - Complete UiPath Enterprise Solution | UiPath
• UiPath Pricing 2024: Its 3 RPA Pricing Models, Clearly Explained

For UiPath license estimation, two key best practices include assessing the number and complexity of processes to determine the required types of robots (e.g., Attended, Unattended, Studio, StudioX) and evaluating concurrent robot usage during peak workloads. Understanding the complexity of the processes helps in choosing the right type of robots that align with the tasks' needs, ensuring efficient automation. Concurrent usage assessment is crucial for optimizing resource allocation, especially during peak periods, to maintain high efficiency and productivity without overprovisioning resources. This strategic approach to licensing can significantly enhance the scalability and cost-effectiveness of RPA initiatives.

References:

• UiPath Licensing Guide: Robot Types and Licensing
• UiPath Best Practices: License Management

A long-running workflow is a type of automation process that requires logical fragmentation or human intervention, such as invoice processing or performance reviews1. Long-running workflows are designed as Orchestration Processes in Studio, using the Persistence and Intelligent.OCR package2. This package contains activities that enable the workflow to suspend and resume execution based on certain conditions, such as waiting for a job, a queue item, a form task, or an external task to be completed1. These activities are called persistence points, and they create checkpoints in the workflow where the process state is saved and restored2. In order for the persistence points to work properly, all the variables used in the scope of a long-running activity must be serializable, meaning that they can be converted into a format that can be stored and retrieved3. Non-serializable variables, such as browser or application objects, cannot be used in long-running workflows, as they would cause errors or inconsistencies3.

References: 1: Orchestrator - Working With Long-running Workflows 2: Action Center - Designing long-running workflows 3: Best practice long running processes - UiPath Community Forum

High-volume, repetitive tasks that are predictable and do not require creativity or critical thinking are well-suited for automation with RPA. These tasks often involve routine data entry, data extraction, and rule-based decision-making, which can be efficiently handled by robots. Automating such tasks can significantly improve efficiency, accuracy, and speed, while freeing up human workers to focus on more complex and value-added activities that require human judgment, creativity, and emotional intelligence.

References:

• UiPath RPA Guide: Identifying Processes for Automation
• UiPath Best Practices: Process Selection

Deal guidance is a machine learning application that helps financial services firms to optimize their deal pricing, terms, and risk assessment. It uses historical data, market trends, and customer preferences to generate insights and recommendations for the best deal outcomes. Deal guidance can also automate the deal approval process and generate reports and dashboards for performance monitoring and compliance. References: UiPath Automation Solution Architect Learning Plan, Module 6: Machine Learning and AI1; UiPath Automation Suite - Deal Guidance2; UiPath Academy - Roles of a Solution Architect in Automation, RPA Setting3.

In Document Classification tasks within UiPath, the primary role of the user is to review the classifications made by the automation (Robot) and make any necessary corrections or approvals. This step is crucial in ensuring the accuracy of the document processing workflow, especially in scenarios where the classification might not be entirely clear-cut or when the document falls into ambiguous categories. The user's input helps in training the model better, improving the automation's accuracy over time. This human-in-the-loop approach ensures that even as the automation handles the bulk of the work, the final say in nuanced or critical classifications comes from a human, maintaining quality and reliability.

References:

• UiPath Document Understanding Guide: Document Classification
• UiPath Academy: Document Understanding Course

Choosing a UiPath Automation Cloud licensing plan should be a strategic decision based on an organization's specific automation needs, budget constraints, and intended use cases. It's essential to assess the scale of automation, the complexity of processes to be automated, and the number of users or robots that will be required. This evaluation helps in selecting a licensing plan that not only fits the current requirements but also offers scalability for future expansion. Rushing into a plan with the most licenses or the longest trial period without considering these factors can lead to underutilization or unexpected costs. A well-chosen plan ensures that the organization can maximize the benefits of automation while staying within budget.

References:

• UiPath Automation Cloud: Licensing Plans
• UiPath Cloud Platform Guide: Choosing the Right Plan

When designing an RPA solution, it's important to focus on maintainability, modularity, scalability, efficient exception handling, and comprehensive error reporting. These principles ensure that the automation solution can be easily updated, extended, and maintained over time. Modularity allows for parts of the solution to be reused across different projects, while scalability ensures that the solution can handle increased workloads as necessary. Efficient exception handling and error reporting are crucial for identifying and resolving issues quickly, ensuring the reliability and stability of the automation.

References:

• UiPath Best Practices: Automation Best Practices
• UiPath Studio Guide: Project Organization

The fields of an entity are used to define the attributes and relationships of the entity, such as the name, type, format, and validation rules of each field. The fields can be of different types, such as text, number, date-time, relationship, auto-number, choice set, and file. The fields can also be customized and modified according to the business needs.

References: Data Service - Entity Field Types, Data Service - Customizing an Entity

Splitting the process into three distinct sub-processes - Dispatcher, Performer, and Reporter - aligns with best practices for RPA design, promoting modularity and scalability. The Dispatcher's role would be to initialize the process, such as identifying and queuing work items. The Performer would then take over to handle the core automation tasks (steps 2 to 4), focusing on processing the queued items. Finally, the Reporter would compile and present the results or outcomes of the automation (steps 5 to 7). This structure not only clarifies the process flow but also facilitates easier maintenance and updates, as each sub-process can be modified independently without affecting the others.

References:

• UiPath REFramework Guide: Dispatcher-Performer Model
• UiPath Best Practices: Framework for Process Design

Option B is the most suitable approach for the process described above, as it uses the Robotic Enterprise Framework (ReFramework) template with TransactionItem set as DataRow and TransactionData as DataTable. This template provides a robust and scalable structure for transactional processes, with built-in mechanisms for exception handling, logging, retrying, and reporting. By setting the TransactionItem as DataRow and the TransactionData as DataTable, the process can read the invoice numbers from the CSV file and process them one by one in a loop. The template also allows the use of queues to store the transaction data and status, which enables the process to resume from the last successful transaction in case of a system exception. This way, the process can ensure that each invoice number is processed only once in each application, and avoid duplicate or skipped transactions. The template also integrates with UiPath Orchestrator, which provides centralized management, monitoring, and scheduling of the process. The template also supports the use of long-running workflows, which can handle human intervention scenarios using UiPath Action Center. The template also complies with the UiPath Automation Solution Architect best practices and standards, such as naming conventions, modularity, reusability, and maintainability. References:

• UiPath Studio - Robotic Enterprise Framework Template
• UiPath Studio - Working with Queues in the ReFramework
• UiPath Studio - Long Running Workflow Template with UiPath Tasks
• [UiPath Automation Solution Architect - Course Overview]
• [UiPath Automation Solution Architect - Best Practices and Standards]

The correct flow from a high level design standpoint in UiPath Test Manager1 is:

Step 1 = Create Project Step 2 = Create Requirement Step 3 = Create TestCase Step 4 = Create TestSet

According to the UiPath documentation and the Test Manager Overview Demo1, the high level design flow in UiPath Test Manager1 involves the following steps:

• Create Project: A project is a container for all the testing artifacts related to a specific application or process. You can create a project in Test Manager by giving it a name, a description, and a type (RPA or Application). You can also import a project from another source, such as Jira or Azure DevOps 2.
• Create Requirement: A requirement is a statement of what the application or process should do or achieve. You can create a requirement in Test Manager by giving it a name, a description, and a custom attribute. You can also import a requirement from another source, such as Jira or Azure DevOps 2.
• Create TestCase: A test case is a set of steps or actions that verify a specific requirement. You can create a test case in Test Manager by giving it a name, a description, a type (manual or automated), and a custom attribute. You can also import a test case from another source, such as Studio or Task Capture 2.
• Create TestSet: A test set is a collection of test cases that are executed together as part of a testing cycle. You can create a test set in Test Manager by giving it a name, a description, a type (manual or automated), and a custom attribute. You can also import a test set from another source, such as Orchestrator 2.

References:

• 2: UiPath Test Manager - UiPath Documentation Portal
• 3: Task Capture - UiPath Test Manager - UiPath Documentation Portal
• 1: Test Manager Overview Demo - Test Automation Case | UiPath

In UiPath's documentation regarding the Dispatcher and Performer model, it is emphasized that the Dispatcher is responsible for adding items to the queue, while the Performer processes these items. An accurate split between Dispatcher and Performer tasks is crucial for efficient process design. The given estimation table should clearly distinguish tasks that add items to the queue (Dispatcher) from those that process items from the queue (Performer). Mixing or inaccurately assigning these tasks can lead to inefficiencies and confusion in process execution. (Source: https://docs.uipath.com/orchestrator/docs/about-queues-and-transactions )