UiPath UiPath-SAIv1 Dumps - Pass UiPath Certified Professional Specialized AI Professional v1.0 Exam in 2026

According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data.However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12.A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.

References:1: Document Classification Training Overview2: Document Classification Training Related Activities3: Training High Performing Models

In UiPath AI Center, the platform supports several types of machine learning (ML) models, including:

Out-of-the-box models from UiPath: Pre-built models designed for common automation tasks.

Models from UiPath technology partners: External models developed by UiPath's partners.

Open-source models: Community-contributed models that can be used and adapted for various use cases.

Custom models: Models that users build and train specifically for their projects using their datasets.

This flexibility in model support ensures that organizations can leverage a wide range of machine learning capabilities to suit different automation needs.

For more details, refer to:

UiPath AI Center Documentation: AI Center Models

Machine Learning Model Types: Types of Models in UiPath AI Center

The valid Action Center statuses are:

Unassigned: The action is not assigned to any user.

Pending: The action is assigned and awaiting user response.

Completed: The action has been resolved or finished.

In UiPath, the Data Scientist role is primarily responsible for creating, training, and uploading machine learning models to AI Center. While RPA developers might integrate these models into workflows, it is the Data Scientist who builds and fine-tunes models, ensuring their accuracy and relevance for various use cases.

According to the UiPath documentation and web search results, OCR (Optical Character Recognition) is a method that reads text from images, recognizing each character and its position. OCR is used to digitize documents and make them searchable and editable. OCR can be performed by different engines, such as Tesseract, Microsoft OCR, Microsoft Azure OCR, OmniPaqe, and Abbyy. OCR is a basic step in the Document Understanding Framework, which is a set of activities and services that enable the automation of document processing workflows.

IntelligentOCR is a UiPath Studio activity package that contains all the activities needed to enable information extraction from documents. Information extraction is the process of identifying and extracting relevant data from documents, such as fields, tables, entities, and labels. IntelligentOCR uses different components, such as classifiers, extractors, validators, and trainers, to perform information extraction. IntelligentOCR also supports different formats, such as PDF, PNG, JPG, TIFF, and BMP. IntelligentOCR is an advanced step in the Document Understanding Framework, which builds on the OCR output and provides more functionality and flexibility.

References:

About the IntelligentOCR Activities Package

OCR Activities

OCR Feature Comparison: Uipath Community vs Uipath Licensed OCR

Document Understanding - Introduction