Most Recent UiPath-SAIv1 Exam Dumps

In UiPath Communications Mining, the term Coverage refers to the proportion of messages in the dataset that have informative label predictions. This is an important metric that indicates how much of the dataset the model is able to classify with meaningful and relevant labels. High coverage means that the model is effectively assigning labels to a large portion of the data, which is crucial for ensuring the model's usefulness in automating communication mining tasks.

For more details, refer to:

UiPath Communications Mining Performance Factors: Model Coverage and Accuracy

Communications Mining: Coverage and Other Metrics

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

The Explore phase is the second phase of model training in UiPath Communications Mining, which is a solution that enables the analysis of large volumes of text-based communications using natural language processing and machine learning. The Explore phase builds on the foundations of the taxonomy that was created in the Discover phase by reviewing clusters and searching for different terms and phrases. The objective of the Explore phase is to provide each of the labels or entities that are important for the use case with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset.The Explore phase is the core phase of model training, and requires the most time and effort, but also leads to better model performance and accuracy1.

References:1:Communications Mining - Explore

When a pipeline is in the running state in UiPath AI Center, the only action that can be performed is to Kill it. This terminates the process immediately, stopping any further processing. Actions like removing or restarting the pipeline can only be performed after the pipeline has stopped. (Source: UiPath Pipeline documentation

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