Ace Your UiPath-SAIAv1 UiPath Specialized AI Associate Exam

 Based on the classification of documents, there are two common types of data extraction methodologies: rule-based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor’s internal taxonomy (if any), and how to implement “fall-back” rules for data extraction2.

UiPath AI Center can be deployed in multiple ways to meet different organizational needs and infrastructures. The available deployment options include:

Cloud availability: Using UiPath's cloud services.

On-premises air-gapped: A fully isolated, offline environment for organizations with strict security requirements.

On-premises online: Deployed on-premise but with internet connectivity.

Hybrid mode: Combining on-premises AI Center with on-premises Orchestrator for flexibility.

Automation Suite: A comprehensive deployment of UiPath tools, including AI Center.

For more details, refer to:

UiPath AI Center Deployment Models: Deployment Options

In UiPath, the Verbose log level offers the most detailed information about the execution of activities. It logs every possible detail about the automation operations, including variable changes, function calls, and external responses. This level is particularly useful for in-depth debugging and analysis.

UiPath Documentation

The hierarchy of log levels in ascending order of priority is as follows:

Off: No logs are stored.

Verbose: Logs all details about automation operations.

Trace: Logs finer-grained informational events than the Debug level.

Information: Logs informational messages that highlight the progress of the application.

Warning: Logs potentially harmful situations.

Error: Logs error events that might still allow the application to continue running.

Fatal: Logs very severe error events that will presumably lead the application to abort.

Therefore, setting the log level to Verbose ensures that all possible details about the execution are captured, aiding in thorough diagnostics.