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[General] New UiPath-SAIAv1 Braindumps Pdf, Exam UiPath-SAIAv1 Topics

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【General】 New UiPath-SAIAv1 Braindumps Pdf, Exam UiPath-SAIAv1 Topics

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q35-Q40):NEW QUESTION # 35
What is the page unit cost per extracted page for the RegEx Extractor?
  • A. 0.2
  • B. 0.5
  • C. 0
  • D. 1
Answer: D
Explanation:
According to the UiPath documentation, the RegEx Extractor is a data extraction method that uses regular expressions to define and capture data from documents1. The RegEx Extractor does not consume any page units, which are the units of measurement for the consumption of Document Understanding services2.
Therefore, the page unit cost per extracted page for the RegEx Extractor is 0.
References:
1: RegEx Extractor 2: Document Understanding - Metering & Charging Logic

NEW QUESTION # 36
Which of the following best describes UiPath Document Understanding?
  • A. A software for creating machine learning models.
  • B. A suite of tools for automating document processing tasks.
  • C. A solution for managing cloud infrastructure.
  • D. A platform for managing robotic process automation (RPA) workflows.
Answer: B
Explanation:
Reference: UiPath Document Understanding

NEW QUESTION # 37
What is the role of the Taxonomy Manager?
  • A. To select the type of ML that can be used in the project.
  • B. To select which extractors are trained for each document type and field.
  • C. To create and edit a Taxonomy file specific to the current automation project.
  • D. To present a document processing specific user interface for validating and correcting automatic classification outputs.
Answer: C
Explanation:
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

NEW QUESTION # 38
Which are the the minimum required inputs in order to configure the Classification Station as an attended activity?
  • A. Taxonomy, Document Path, Document Object Model, Document Text, Automatic Classification Results.
  • B. Taxonomy, Document Path, Document Object Model, Document Text.
  • C. Taxonomy, Document Object Model, Automatic Extraction Results, Document Directory.
  • D. Taxonomy, Document Path, Document Directory, Document Text, Automatic Extraction Results.
Answer: B
Explanation:
To configure the Classification Station as an attended activity in UiPath, certain inputs are mandatory for proper functionality. These include:
Taxonomy: The schema that defines the structure of document types and fields.
Document Path: The file path of the document to be classified.
Document Object Model (DOM): Generated from the document using the Digitize Document activity, this is a structured representation of the document.
Document Text: The extracted text of the document, also an output from the Digitize Document activity.
These inputs allow the Classification Station to review and validate the classification results, eithermanually or based on automatic suggestions from previous processes.

NEW QUESTION # 39
What is the default visibility of an ML skill?
  • A. An ML skill is by default private and can be made public.
  • B. An ML skill is by default private and can't be made public.
  • C. An ML skill is by default public and can be made private.
  • D. An ML skill is by default public and can't be made private.
Answer: A
Explanation:
An ML skill is a consumer-ready, live deployment of an ML or OS package that can be used in RPA workflows. By default, an ML skill is private, which means it can only be accessed by the users who have the permission to view and manage the project that contains the skill. However, an ML skill can be made public by enabling the Public Skill option in the ML Skill Details page. This will generate a public URL and an API key for the skill, which can be used to access the skill from any external system or application12.
References: 1: AI Center - About ML Skills 2: Make ML Skills and Datasets public via URL + API Key - Preview

NEW QUESTION # 40
......
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