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

UiPath AI Center offers three primary package types: pre-trained, custom training, and fine-tunable models. These are essential for various use cases, from leveraging existing models to training custom ones based on specific data

The Taxonomy Manager is a tool that enables you to create and edit a Taxonomy file, which is an XML file that defines the document types and fields that are relevant for your automation project1. The Taxonomy file is used by the Classify Document Scope and Data Extraction Scope activities to perform document classification and data extraction, respectively2. The Taxonomy Manager allows you to add, remove, rename, or reorder document types and fields, as well as specify the data type, format, and validation rules for each field3. The Taxonomy Manager also provides a preview of the Taxonomy file and a validation feature to check for errors or inconsistencies.

References:

1: About Taxonomy Manager 2: About Document Understanding Framework 3: Using the Taxonomy Manager : Taxonomy Manager User Interface Description

 According to the UiPath documentation, there are two common types of data extraction methodologies used in document understanding processes: rule-based data extraction and model-based data extraction12. Rule-based data extraction targets structured documents, such as forms, invoices, or receipts, that have a fixed layout and a predefined set of fields. Rule-based data extraction uses predefined rules, such as regular expressions, keywords, or coordinates, to locate and extract the relevant data from the documents1. Model-based data extraction is used to process semi-structured and unstructured documents, such as contracts, emails, or reports, that have a variable layout and a diverse set of fields. Model-based data extraction uses machine learning models, such as neural networks, to learn from examples and extract the relevant data from the documents1. Both methodologies have their advantages and limitations, and depending on the use case, they can be used separately or in combination, in a hybrid approach2.

References: 1: Data Extraction Overview 2: Document Processing with Improved Data Extraction

The Configure Extractors Wizard is a tool that enables you to select and customize the extractors that are used for data extraction from documents. It is accessed via the Data Extraction Scope activity, which is a container for extractor activities. The wizard allows you to map the fields defined in your taxonomy with the fields supported by each extractor, and to set the minimum confidence level and the framework alias for each extractor. The wizard is mandatory for the extractor configuration, as it ensures that the extractors are applied correctly to each document type and field1.

References: Configure Extractors Wizard of Data Extraction Scope

When exporting data in UiPath Document Understanding workflows, additional information such as the page number and the exact position on the page can be included. This metadata is particularly useful for auditing or validating the extracted data.

In UiPath AI Center, datasets can only be deleted if they are not being utilized in any active pipelines. This ensures that datasets tied to running or scheduled operations are not inadvertently deleted, which could disrupt ongoing processes. Once the dataset is confirmed to be unused in any active pipeline, it can safely be removed​

The Document Understanding Process uses two types of synchronization mechanisms to prevent multiple robots from writing in a file at the same time: file locks and queues. File locks are used to ensure that only one robot can access a file at a time, while queues are used to store the information extracted from the documents and to avoid data loss or duplication. The process uses the following activities to implement these mechanisms:

File Lock Scope: This activity creates a lock on a file or folder and executes a set of activities within it. The lock is released when the scope ends or when an exception occurs. This activity ensures that only one robot can read or write a file or folder at a time, and prevents other robots from accessing it until the lock is released.

Add Queue Item: This activity adds an item to a queue in Orchestrator, along with some relevant information, such as a reference, a priority, or a deadline. The item can be a JSON object, a string, or any serializable .NET type. This activity ensures that the information extracted from the documents is stored in a queue and can be retrieved by another robot or process later.

Get Queue Items: This activity retrieves a collection of items from a queue in Orchestrator, based on some filters, such as status, reference, or creation time. The items can be processed by the robot or passed to another activity, such as Update Queue Item or Delete Queue Item. This activity ensures that the information stored in the queue can be accessed and manipulated by the robot or process.

References: Document Understanding Process: Studio Template, File Lock Scope, Add Queue Item, [Get Queue Items]