UiPath UiPath-ADPv1 Exam With Confidence Using Practice Dumps

The correct option about a State Machine layout is that it can have only one initial state and multiple final states. A State Machine is a type of workflow that consists of a set of states, transitions, and triggers. A state represents a stage of the process, a transition represents a change from one state to another, and a trigger represents a condition or an event that activates a transition. A State Machine can have only one initial state, which is the starting point of the workflow, and one or more final states,which are the end points of the workflow. A State Machine can also have intermediate states, which are the states between the initial and the final states. A State Machine can have multiple paths and branches, depending on the logic and the triggers of the workflow.

Starting with UiPath Studio version 2022.10, dependencies that lack Windows compatibility are marked as unresolved when they are converted in a project. This means that the project cannot be executed until the unresolved dependencies are manually resolved by the developer. This is done to ensure that the project can run on both Windows and Linux platforms without compatibility issues. References: [UiPath Studio Guide - Converting Projects to Linux Compatibility]

AI Computer Vision is a feature of UiPath that enables the automation of remote applications or desktops, such as Citrix Virtual Apps, Windows Remote Desktop, or VMware Horizon, by using native selectors. Native selectors are expressions that identify UI elements reliably and accurately, without relying on OCR or image recognition activities1. AI Computer Vision uses a mix of AI, OCR, text fuzzy-matching, and an anchoring system to visually locate elements on the screen and interact with them via UiPath Robots, simulating human interaction2. AI Computer Vision is especially useful for scenarios where the UI elements have low quality or scaling issues, which make them difficult to recognize with traditional methods3.

Option A is an accurate example of using AI Computer Vision features in a UiPath project, because it involves identifying and interacting with UI elements in a remote desktop application, which is one of the main use cases of AI Computer Vision. By using the Computer Vision activities, such as CV Screen Scope, CV Click, CV Get Text, etc., you can automate tasks in a remote desktop application without using selectors, OCR, or image recognition activities4.

The other options are not accurate examples of using AI Computer Vision features in a UiPath project, because they involve tasks that are not related to the automation of remote applications or desktops, or that do not use the Computer Vision activities. For example:

Option B involves training a custom machine learning model, which is not a feature of AI Computer Vision, but of the UiPath AI Fabric, a platform that enables you to deploy, consume, and improve machine learning models in UiPath.

Option C involves extracting plain text from a scanned PDF document, which is not a feature of AI Computer Vision, but of the UiPath Document Understanding, a framework that enables you to classify, extract, and validate data from various types of documents.

Option D involves performing sentiment analysis on a text string, which is not a feature of AI Computer Vision, but of the UiPath Text Analysis, a set of activities that enable you to analyze the sentiment, key phrases, and language of a text using pre-trained machine learning models.