EXAM UIPATH-SAIV1 LABS - UIPATH FIRST-GRADE BOOKS UIPATH-SAIV1 PDF 100% PASS

Exam UiPath-SAIv1 Labs - UiPath First-grade Books UiPath-SAIv1 PDF 100% Pass

Exam UiPath-SAIv1 Labs - UiPath First-grade Books UiPath-SAIv1 PDF 100% Pass

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q36-Q41):

NEW QUESTION # 36
What is the name of the web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models?

  • A. Data Manager.
  • B. Digitization.
  • C. Document Manager.
  • D. ML Extractor.

Answer: A

Explanation:
Data Manager is a web application that allows users to prepare, review, and make corrections to datasets required for Machine Learning models. Data Manager enables users to create and manage datasets, label data, validate and export data, and monitor data quality and progress. Data Manager supports various types of data, such as documents, images, text, and tables. Data Manager is integrated with AI Center, where users can train and deploy Machine Learning models using the datasets created or modified in Data Manager12.
References: 1: Data Manager Overview 2: AI Center - About Datasets


NEW QUESTION # 37
Which scenario would be best accomplished using unattended automation?

  • A. Resolving customer queries with real-time input from support agents.
  • B. Generating invoices that require manual approval before issuing.
  • C. Running reports and sending them via email to stakeholders overnight.
  • D. Providing on-demand assistance to users for specific activities.

Answer: C

Explanation:
Unattended automation is ideal for tasks that can run independently without human intervention, such as scheduled activities during off-hours. Running reports and emailing stakeholders overnight is a perfect use case for unattended bots, which can execute the process autonomously.


NEW QUESTION # 38
When processing a document type that comes in a high variety of layouts, what is the recommended data extraction methodology?

  • A. Rule-based data extraction.
  • B. Model-based data extraction.
  • C. Manual data extraction.
  • D. Hybrid data extraction.

Answer: D

Explanation:
Based on the classification of documents, there are two common types of data extraction methodologies: rule- based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor's internal taxonomy (if any), and how to implement "fall-back" rules for data extraction2.
References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction


NEW QUESTION # 39
Which role is responsible for building and uploading ML (Machine Learning) models lo Al Center?

  • A. RPA Developer.
  • B. Administrator.
  • C. Process Controller.
  • D. Data Scientist.

Answer: D

Explanation:
In UiPath, the Data Scientist role is primarily responsible for creating, training, and uploading machine learning models to AI Center. While RPA developers might integrate these models into workflows, it is the Data Scientist who builds and fine-tunes models, ensuring their accuracy and relevance for various use cases.


NEW QUESTION # 40
What information does the comparison between two cohorts display on the Comparison page in UiPath Communications Mining?

  • A. Entity count for each metadata.
  • B. Total verbatim count and proportion for each label.
  • C. Differences in verbatim length between Group A and Group B.
  • D. Verbatim content for each label.

Answer: B

Explanation:
According to the UiPath documentation, UiPath Communications Mining is a tool that enables you to analyze text-based communications data, such as customer feedback, support tickets, or chat transcripts, using natural language processing (NLP) and machine learning (ML) techniques1. One of the features of UiPath Communications Mining is the Comparison page, which allows you to compare two cohorts of verbatims based on different criteria, such as date range, source, metadata, or label2. The Comparison page displays the following information for each cohort3:
* Total verbatim count: The number of verbatims in the cohort.
* Proportion for each label: The percentage of verbatims in the cohort that are assigned to each label. A label is a category or a topic that is relevant for the analysis, such as sentiment, intent, or issue type.
Labels can be predefined or custom-defined by the user.
* Statistical significance: The p-value that indicates whether the difference in proportions between the two cohorts is statistically significant or not. A p-value less than 0.05 means that the difference is unlikely to be due to chance.
The Comparison page also provides a visual representation of the proportions for each label using a bar chart, and allows the user to drill down into the verbatim content for each label by clicking on the bars3. Therefore, the correct answer is A.
References:
1: About Communications Mining 2: Communications Mining - Comparing Cohorts 3: Communications Mining - Comparison Page


NEW QUESTION # 41
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