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UiPath-SAIv1 Exams Dumps - Latest Study UiPath-SAIv1 Questions

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q111-Q116):

NEW QUESTION # 111
What is the Document Object Model (DOM) in the context of Document Understanding?

A. The DOM is a JSON object containing information such as name, content type, text length, number of pages, page rotation, detected language, content, and coordinates for the words identified in the file.
B. The DOM is a built-in artificial intelligence system that automatically understands and interprets the content and the type of documents, eliminating the need for manual data extraction.
C. The DOM is a graphical user interface (GUI) tool in UiPath Document Understanding that provides visual representations of documents, making it easier for users to navigate and interact with the content.
D. The DOM is a feature that allows you to convert physical documents into virtual objects that can be manipulated using programming code.

Answer: A

Explanation:
The Document Object Model (DOM) is a data representation of the objects that comprise the structure and content of a document on the web1. In the context of Document Understanding, the DOM is a JSON object that is generated by the Digitize Document activity, which uses the UiPath Document OCR engine to extract the text and layout information from the input document2. The DOM contains the following properties for each document3:
* name: The name of the document file.
* contentType: The MIME type of the document file, such as application/pdf or image/jpeg.
* textLength: The number of characters in the document text.
* pages: An array of objects, each representing a page in the document. Each page object has the following properties:
* pageNumber: The number of the page, starting from 1.
* rotation: The angle of rotation of the page, in degrees. A positive value indicates clockwise rotation, and a negative value indicates counterclockwise rotation.
* language: The language code of the page, such as en or fr.
* content: An array of objects, each representing a word or a line in the page. Each content object has the following properties:
* type: The type of the content, either word or line.
* text: The text of the content.
* boundingBox: An array of four numbers, representing the coordinates of the top-left and bottom-right corners of the content, in the format [x1, y1, x2, y2]. The coordinates are relative to the page, with the origin at the top-left corner, and the unit is pixel.
* confidence: A number between 0 and 1, indicating the confidence level of the OCR engine in recognizing the content.
The DOM can be used as an input for other activities in the Document Understanding framework, such as Classify Document Scope, Data Extraction Scope, or Export Extraction Results. The DOM can also be manipulated using programming code, such as JavaScript or Python, to perform custom operations on the document data.
References:
1: Introduction to the DOM - Web APIs | MDN 2: Digitize Document 3: Document Object Model

NEW QUESTION # 112
Which of the following is an indicator that sufficient training has been completed for a model in UiPath Communications Mining?

A. A model rating of 70-90 or better.
B. A model rating of 50-60.
C. A model rating of 30-40.
D. A model rating of 40-50.

Answer: A

Explanation:
The model rating is a proprietary score that assesses the overall health and performance of a model in UiPath Communications Mining. It considers four main factors: balance, underperforming labels, coverage, and all labels. The model rating is a score from 0 to 100, which equates to a rating of 'Poor' (0-49), 'Average' (50-
69), 'Good' (70-89) or 'Excellent' (90-100). A model rating of 70-90 or better indicates that the model has sufficient training and performs well in all of the most important areas. A model rating of 70-90 or better also means that the model has a balanced and representative training data, a low number of labels with performance issues or warnings, a high coverage of the dataset by informative labels, and a high average precision of all labels.
References: Communications Mining - Model Rating, Communications Mining - Understanding and improving model performance

NEW QUESTION # 113
How do you load a taxonomy from a given non-default location text file into a variable?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

to load a taxonomy from a given non-default location text file into a variable, the order of steps should be as follows:
* Create a new variable of type 'DocumentTaxonomy' called 'documentTaxonomy', then create a new string variable called 'taxonomyPath'.
* This step involves setting up the necessary variables that will be used in the process. The
'documentTaxonomy' variable will hold the deserialized taxonomy object, and 'taxonomyPath' will store the path to the taxonomy file.
* Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.
* Here you will assign the path of the taxonomy file to the 'taxonomyPath' variable.
* Use the Read Text File activity on the 'taxonomyPath' variable created. Next, create a new variable 'taxonomyText', then save the output of the Read Text File activity to 'taxonomyText'.
* This step is where you read the contents of the taxonomy file using the 'Read Text File' activity.
The contents are stored in the 'taxonomyText' variable.
* Inside the last Assign activity 'Value to Save' property add the following expression: "Newtonsoft.
Json.JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText)" where ' taxonomyText' is the text read from the file and 'documentTaxonomy' (the most recent variable created) is the variable you created.
* In this final step, you will deserialize the JSON content from the 'taxonomyText' into a
'DocumentTaxonomy' object using the 'JsonConvert.DeserializeObject' method and assign it to the 'documentTaxonomy' variable.
Following these steps in this order will load the taxonomy from a text file into the 'documentTaxonomy' variable in UiPath.

NEW QUESTION # 114
How is the Taxonomy component used in the Document Understanding Template?

A. To apply rigor in the taxonomy of data, ensuring any newly discovered object fits into one and only one category or object.
B. To organize knowledge by using a controlled vocabulary to make it easier to find related information.
C. To apply relationship schemas other than parent-child hierarchies, such as network structures on the processed data.
D. To define the document types and the pieces of information targeted for data extraction (fields) for each document type.

Answer: D

Explanation:
According to the UiPath documentation, the Taxonomy component is used in the Document Understanding Template to define the document types and the fields that are targeted for data extraction for each document type. The Taxonomy component is the metadata that the Document Understanding framework considers in each of its steps, such as document classification and data extraction. The Taxonomy component allows you to create, edit, import, or export the taxonomy of your project, which is a collection of document types and fields that suit your specific objectives. The Taxonomy component also allows you to configure the field types, details, and validations, as well as the supported languages and categories for your documents.
References:
* Document Understanding - Taxonomy
* Document Understanding - Taxonomy Overview
* Document Understanding - Create and Configure Fields

NEW QUESTION # 115
What new capability has been introduced for processing unstructured documents in the 2023.10 release?

A. Real-time document translation.
B. Manual classification of document types.
C. Only using pre-trained extraction models.
D. Generative capabilities for classifying various document types.

Answer: D

Explanation:
In the 2023.10 release of UiPath, a significant new feature is the introduction of generative capabilities for classifying various document types. This update allows for more advanced handling of unstructured data by leveraging AI to automatically generate classifications, even for document types that the model hasn't encountered before. This feature enhances the flexibility and power of Document Understanding, enabling organizations to handle a wider variety of documents without needing extensive manual setup for each new type.
(Source: UiPath 2023.10 Release Notes)

NEW QUESTION # 116
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