Newly Released UiPath UiPath-ASAPv1 Exam PDF

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

According to the UiPath documentation, making an ML Skill public allows it to be accessible via an endpoint from outside of UiPath environment. This means that you can call it without the need to go through a robot connected to the specific tenant1. To do that, you need to select Modify current deployment in the ML Skill Details page > Update skill window, and enable the Public skill option. This will generate an API Key and URL that you can use to connect to the ML Skill from a different tenant1.

References:

• AI Center - Managing ML Skills
• What are the steps in moving a ML Skill to a new Tenant?
• AI Center ML Skill export question
• UiPath tenant move for AI Center

UiPath Automation Cloud is a cloud-based enterprise automation solution that lets you start and scale your automation from the cloud with SaaS robots and self-hosted robots. It offers security, compliance, governance, and insights for your RPA initiatives. It does not require manual installation and maintenance, as it is delivered as a service. It does not have single or multi-node deployment options, as it is hosted on UiPath’s infrastructure. It does not provide major updates twice yearly, as it is updated continuously with new features and improvements.

References:

• Complete cloud-based enterprise automation solution | UiPath
• Automation Cloud - UiPath Documentation Portal
• Automation Cloud - About Automation Cloud - UiPath Documentation Portal

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