UiPath-ADPv1 Exam Dumps : UiPath (ADPv1) Automation Developer Professional Exam (2023.10)

The "Step Out" action in UiPath Studio's debugging tools allows the developer to finish executing the current container (such as a sequence, loop, or workflow) and returns to the next higher level in the call stack. Essentially, it continues the execution of activities within the current container without stepping into any invoked workflows or activities.

The correct functionality provided by the "Step Out" action is:

D. Executes activities in the current container and then pauses the execution after stepping out to the next higher level in the call stack.

UiPath Orchestrator provides integration capabilities with various credential stores to securely store and manage credentials such as usernames and passwords, API keys, and other secrets. The built-in support for credential stores in Orchestrator includes:

•Azure Key Vault: A cloud service provided by Microsoft that safeguards cryptographic keys and other secrets used by cloud applications and services.

•CyberArk Credential Provider (CCP): CyberArk's enterprise-grade solution designed to secure, manage, and monitor privileged accounts and credentials.

•HashiCorp Vault: A tool for secrets management, encrypting sensitive data, and access control across dynamic infrastructure.

•Thycotic Secret Server: An enterprise-level solution for privileged account management and protection of critical enterprise passwords and secrets.

•BeyondTrust: A privileged access management platform that secures and manages access to critical assets, passwords, and endpoints.

•CyberArk: In addition to the CCP, Orchestrator supports integration with other CyberArk solutions for secure credential management.

These integrations allow Orchestrator to retrieve credentials securely at runtime for use in automation workflows, enhancing the security posture by avoiding hard-coded or plaintext credentials in automation scripts.

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.