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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q92-Q97):

NEW QUESTION # 92
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?

A. View, save, and modify dataset model versions.
B. Train models using unsupervised learning.
C. Monitor model performance and receive recommendations.
D. Access detailed, queryable charts, statistics, and customizable dashboards.

Answer: D

NEW QUESTION # 93
Why should using the Search feature be limited when training in UiPath Communications Mining?

A. It could increase model bias.
B. It could increase model coverage.
C. It could decrease model bias.
D. It could decrease model coverage.

Answer: A

Explanation:
In UiPath Communications Mining, over-reliance on the Search feature during the training process can lead to an increase in model bias. This happens because using search-based filtering to identify and label examples might not represent the full diversity of the data. The model could be trained on a skewed subset of the data, causing it to favor certain patterns or keywords, and thus biasing the model towards specific types of data rather than learning to generalize effectively across all data.
Limiting the use of search ensures that the training process considers a broader and more representative sample of the data, which reduces the risk of introducing bias into the model and helps it generalize better to new, unseen communications.
For more details, refer to:
* UiPath Communications Mining Documentation: Model Training and Avoiding Bias

NEW QUESTION # 94
What is the definition of a UiPath Communications Mining data source?

A. The model that we create when training the platform to understand the data in those sources.
B. A user-permissioned project containing a taxonomy with labels and entities.
C. A permissioned storage area within the platform which contains communications and labels.
D. A collection of raw unlabeled communications data of a similar type, that can be associated with up to
10 datasets.

Answer: D

Explanation:
According to the UiPath documentation, a data source is a raw collection of verbatims, which are text-based communications such as survey responses, emails, transcripts, or calls1. A data source can be of a similar type and share a similar intended purpose, such as capturing customer feedback or servicing requests2. A data source can be added to up to 10 different datasets, which are collections of sources and labels that are used to train and evaluate ML models3. Therefore, the correct definition of a UiPath Communications Mining data source is A.
References:
1: Communications Mining - Sources 2: Communications Mining - Managing Sources and Datasets 3: Communications Mining - Understanding the data structure and permissions

NEW QUESTION # 95
What happens during the Classify stage of the Document Understanding Framework?

A. The OCR engine is used to extract text from the image document.
B. The documents are included in one of the taxonomy document types or skipped.
C. The extracted data is exported as a dataset.
D. The target fields are extracted from the document and sent to Action Center for human validation.

Answer: B

Explanation:
According to the UiPath documentation, the Classify stage of the Document Understanding Framework is used to automatically determine what document types are found within a digitized file. The document types are defined in the project taxonomy, which is a collection of all the labels and fields applied to the documents in a dataset. The Classify stage uses one or more classifiers, which are algorithms that assign document types to files based on their content and structure. The classifiers can be configured and executed using the Classify Document Scope activity, which also allows for document type filtering, taxonomy mapping, and minimum confidence threshold settings. The Classify stage outputs the classification information in a unified manner, irrespective of the source of classification. The documents that are classified are then sent to the next stage of the framework, which is Data Extraction. The documents that are not classified or skipped are either excluded from further processing or sent to Action Center for human validation and correction.
References:
* Document Understanding - Document Classification Overview
* Document Understanding - Introduction
* Generative Extraction & Classification using Document Understanding in Cross-Platform Projects (Public Preview)

NEW QUESTION # 96
What components are part of the Document Understanding Process template?

A. Load Document. Categorization. Data Extraction, and Validation.
B. Import. Classification. Text Extractor, and Data Validation.
C. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.
D. Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.

Answer: D

Explanation:
The Document Understanding Process template is a fully functional UiPath Studio project template based on a document processing flowchart. It provides logging, exception handling, retry mechanisms, and all the methods that should be used in a Document Understanding workflow, out of the box. The template has an architecture decoupled from other connected automations and supports both attended and unattended processes with human-in-the-loop validation via Action Center. The template consists of the following components1:
* Load Taxonomy: This component loads the taxonomy file that defines the document types and fields to be extracted. The taxonomy file can be created using the Taxonomy Manager in Studio or the Data Manager web application.
* Digitization: This component converts the input document into a digital format that can be processed by the subsequent components. It uses the Digitize Document activity to perform OCR (optical character recognition) on the document and obtain a Document Object Model (DOM).
* Classification: This component determines the document type of the input document using the Classify Document Scope activity. It can use either a Keyword Based Classifier or a Machine Learning Classifier, depending on the configuration. The classification result is stored in a ClassificationResult variable.
* Data Extraction: This component extracts the relevant data from the input document using the Data Extraction Scope activity. It can use different extractors for different document types, such as the Form Extractor, the Machine Learning Extractor, the Regex Based Extractor, or the Intelligent Form Extractor. The extraction result is stored in an ExtractionResult variable.
* Data Validation: This component allows human validation and correction of the extracted data using the Present Validation Station activity. It opens the Validation Station window where the user can review and edit the extracted data, as well as provide feedback for retraining the classifiers and extractors. The validated data is stored in a DocumentValidationResult variable.
* Export: This component exports the validated data to a desired output, such as an Excel file, a database, or a downstream process. It uses the Export Extraction Results activity to convert the DocumentValidationResult variable into a DataTable variable, which can then be manipulated or written using other activities.
References: Document Understanding Process: Studio Template, Document Understanding Process - New Studio Template, Document Understanding Process Template in UiPath Studio

NEW QUESTION # 97
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Emily Young Emily Young

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