UiPath-SAIv1 UiPath Certified Professional Specialized AI Professional v1.0 Dumps
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Sample Question 4
What is the Machine Learning Extractor?
A. A specialized model that can recognize multiple languages in the same document usingAPI calls to a Hugging Face model with over 250 languages. B. An extraction model that can be enabled and trained in Al Center. For better accuracy.25 documents per model are recommended to train the model. C. A tool using machine learning models to identify and report on data targeted for dataextraction. D. A tool that helps extract data from different document structures, and is particularlyuseful when the same document has multiple formats.
Answer: D
Explanation: The Machine Learning Extractor utilizes machine learning models to
effectively extract data from documents, especially when dealing with varying structures or
formats within the same document type. This capability is crucial in scenarios where
documents do not follow a strict template and have variations in their layout or content
organization. The extractor can be trained to understand these variations and accurately
extract the needed information.
The Machine Learning Extractor is a data extraction tool that uses machine learning
models to extract data from various types of documents, such as invoices, receipts, or
forms. It is especially useful when the same document type has multiple layouts or formats,
as it can learn and infer the values for the targeted fields, even from documents and
layouts it has never seen before1.
The Machine Learning Extractor can be used with one of UiPath’s public Document
Understanding endpoints, which provide generic models for certain document types, or with
custom trained machine learning models hosted in AI Center, which can be tailored to specific use cases. The Machine Learning Extractor can be configured and trained using
the Data Extraction Scope activity in UiPath Studio2.
Which of the following are unstructured documents?
A. Invoices, receipts, purchase orders, and medical bills. B. Banking forms, tax forms, surveys, and identity cards. C. Contracts, emails, banking forms, and tax forms. D. Contracts, agreements, and emails.
Answer: D
Explanation: Unstructured documents are those that do not have a predefined format or
layout, and therefore cannot be easily processed by traditional methods. They often contain
free-form text, images, tables, and other elements that vary from document to document.
Examples of unstructured documents include contracts, agreements, emails, letters,
reports, articles, and so on. UiPath Document Understanding is a solution that enables the
processing of unstructured documents using AI-powered models and RPA workflows1.
The other options are not correct because they are examples of structured or semistructured
documents. Structured documents are those that have a fixed format or layout,
and can be easily processed by rules-based methods. They often contain fields, labels, and values that are consistent across documents. Examples of structured documents include
banking forms, tax forms, surveys, identity cards, and so on. Semi-structured documents
are those that have some elements of structure, but also contain variations or unstructured
content. They often require a combination of rules-based and AI-powered methods to
process. Examples of semi-structured documents include invoices, receipts, purchase
orders, medical bills, and so on2.
References: 1: Unstructured Data Analysis with AI, RPA, and OCR | UiPath 2: Structured,
semi structured, unstructured sample documents for UiPath document understanding -
Studio - UiPath Community Forum
Sample Question 6
Which environment variable is relevant for Evaluation pipelines?
A. eval.enable_ocr B. eval.redo_ocr C. eval.enable_qpu D. eval.use_cuda
Answer: B
Explanation: The environment variable eval.redo_ocr is relevant for Evaluation pipelines because it allows you to rerun OCR when running the pipeline to assess the impact of OCR
on extraction accuracy. This assumes an OCR engine was configured when the ML
Package was created. The other options are not valid environment variables for Evaluation
What is the name of the web application that allows users to prepare, review, and makecorrections to datasets required for Machine Learning models?
A. Document Manager. B. Digitization. C. Data Manager. D. ML Extractor.
Answer: C
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
Sample Question 8
How can the code be tested in a development or testing environment in the context of theDocument Understanding Process?
A. Use them as a template to create other tests. B. Simply run the existing tests C. Based on the use case developed, create test data to test existing and new tests. D. Based on the use case developed, create test data to test existing tests.
Answer: C
Explanation: According to the UiPath Document Understanding Process template, the
best way to test the code in a development or testing environment is to create test data
based on the use case developed, and use it to test both the existing and the new tests.
The test data should include different document types, formats, and scenarios that reflect
the real-world data that the process will handle in production. The existing tests are
provided by the template and cover the main functionalities and components of the
Document Understanding Process, such as digitization, classification, data extraction,
validation, and export. The new tests are created by the developer to test the
customizations and integrations that are specific to the use case, such as custom
extractors, classifiers, or data consumption methods. The test data and the test cases should be updated and maintained throughout the development lifecycle to ensure the
quality and reliability of the code.
References:
Document Understanding Process: Studio Template
Document Understanding Process: User Guide
Sample Question 9
What is one of the purposes of the Config file in the UiPath Document UnderstandingTemplate?
A. It contains the configuration settings for the UiPath Robot and Orchestrator integration. B. It stores the API keys and authentication credentials for accessing external services. C. It specifies the output file path and format for the processed documents. D. It defines the input document types and formats supported by the template.
Answer: B
Explanation: The Config file in the UiPath Document Understanding Template is a JSON
file that contains various parameters and values that control the behavior and functionality
of the template. One of the purposes of the Config file is to store the API keys and
authentication credentials for accessing external services, such as the Document
Understanding API, the Computer Vision API, the Form Recognizer API, and the Text
Analysis API. These services are used by the template to perform document classification,
data extraction, and data validation tasks. The Config file also allows the user to customize
the template according to their needs, such as enabling or disabling human-in-the-loop
validation, setting the retry mechanism, defining the custom success logic, and specifying
the taxonomy of document types.
References: Document Understanding Process: Studio Template, Automation Suite -
Document Understanding configuration file
Sample Question 10
Which of the following OCR (Optical Character Recognition) engines is not free of charge?
A. Tesseract. B. Microsoft Azure OCR. C. OmniPaqe. D. Microsoft OCR.
Answer: C
Explanation: According to the UiPath documentation, OmniPaqe is a paid OCR engine
that requires a license to use. It is one of the most accurate and reliable OCR engines
available, and it supports over 200 languages. The other OCR engines listed are free of
charge, but they may have different features, limitations, and performance levels. For
example, Tesseract is an open-source OCR engine that supports over 100 languages, but
it may not be as accurate as OmniPaqe. Microsoft Azure OCR and Microsoft OCR are both
cloud-based OCR engines that use Microsoft’s technology, but they have different
capabilities and pricing models. Microsoft Azure OCR can process both printed and
handwritten text, and it uses a pay-as-you-go model based on the number of transactions.
Microsoft OCR can only process printed text, and it is included in the UiPath Studio license.
What do entity predictions refer to within UiPath Communications Mining?
A. The understanding of the parent-label relationship when assigning label predictions. B. The difference between label suggestions and label predictions in a training process. C. The identification of a specific span of text as a value for a particular entity type. D. The model's confidence that a specific concept exists within a communication.
Answer: C
Explanation: Entity predictions refer to the process of identifying and highlighting a
specific span of text within a communication that represents a value for a predefined entity type. For example, an entity type could be “Organization” and an entity value could be
“UiPath”. Entity predictions are made by the platform based on the training data and the
rules defined for each entity type. Users can review, accept, reject, or modify the entity
predictions using the Classification Station interface12.
References: Communications Mining - Reviewing and applying entities, Communications
What can be done in the Reports section of the dataset navigation bar in UiPathCommunication Mining?
A. Train models using unsupervised learning. B. View, save, and modify dataset model versions. C. Monitor model performance and receive recommendations. D. Access detailed, quervable charts, statistics, and customizable dashboards.
Answer: D
Explanation: The Reports section of the dataset navigation bar in UiPath Communication
Mining allows users to access detailed, quervable charts, statistics, and customizable
dashboards that provide valuable insights and analysis on their communications data1. The
Reports section has up to six tabs, depending on the data type, each designed to address
different reporting needs2:
Dashboard: Users can create custom dashboard views using data from other tabs,
such as label summary, trends, segments, threads, and comparison. Dashboards
are specific to the dataset and can be edited, deleted, or renamed by users with
Label Summary: Users can view high-level summary statistics for labels, such as volume, precision, recall, and sentiment. Users can also filter by data type, source,
date range, and label category.
Trends: Users can view charts for verbatim volume, label volume, and sentiment
over a selected time period. Users can also filter by data type, source, date range,
and label category.
Segments: Users can view charts comparing label volumes to verbatim metadata
fields, such as sender domain, channel, or language. Users can also filter by data
Using Segments] : [Communications Mining - Using Threads] : [Communications Mining -
Using Comparison]
Sample Question 13
Why might labels have bias warnings in UiPath Communications Mining, even with 100%precision?
A. They were trained using the "Search" option extensively. B. They were trained using the "Shuffle" option extensively. C. They have low recall. D. They lack training examples.
Answer: D
Explanation:
Labels in UiPath Communications Mining are user-defined categories that can be applied
to communications data, such as emails, chats, and calls, to identify the topics, intents, and
sentiments within them1. Labels are trained using supervised learning, which means that
users need to provide examples of data that belong to each label, and the system will learn
from these examples to make predictions for new data2. However, not all labels are equally
easy to train, and some may require more examples than others to achieve good
performance. Labels that have bias warnings are those that have relatively low average
precision, not enough training examples, or were labelled in a biased manner3. Precision is
a measure of how accurate the predictions are for a given label, and it is calculated as the
ratio of true positives (correct predictions) to the total number of predictions made for that
label. A label with 100% precision means that all the predictions made for that label are correct, but it does not necessarily mean that the label is well-trained. It could be that the
label has very few predictions, or that the predictions are only made on a subset of data
that is similar to the training examples. This could lead to overfitting, which means that the
label is too specific to the training data and does not generalize well to new or different
data. Therefore, labels with 100% precision may still have bias warnings if they lack
training examples, because this indicates that the label is not representative of the
underlying data distribution, and may miss important variations or nuances that could affect
the predictions. To improve the performance and reduce the bias of these labels, users
need to provide more and diverse examples that cover the range of possible scenarios and
expressions that the label should capture.
References: 1: Communications Mining Overview 2: [Creating and Training
Labels] 3: Understanding and Improving Model Performance : [Precision and Recall] :
[Overfitting and Underfitting] : Fixing Labelling Bias With Communications Mining
Sample Question 14
Which technology enables UiPath Communications Mining to analyze and enable action onmessages?
A. Natural Language Processing (NLP) B. Virtual Reality. C. Cloud Computing. D. Robotic Process Automation
Answer: A
Explanation: UiPath Communications Mining is a new capability to understand and
automate business communications. It uses state-of-the-art AI models to turn business
messages—from emails to tickets—into actionable data. It does this in real time and on all
major business communications channels1. Natural Language Processing (NLP) is the
branch of AI that deals with analyzing, understanding, and generating natural
language. NLP enables UiPath Communications Mining to extract the most important data
from any message, such as reasons for contact, data fields, and sentiment2. NLP also
allows UiPath Communications Mining to deploy custom AI models in hours, not weeks, by
using automatic labeling and annotation2.
References: 2 Communications Mining - Automate Business Communications |
A. Applying OCR on a 10-page document. B. Creation of a Document Validation Action in Action Center. C. Using ML Classifier on a 21-page document. D. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.
Answer: A
Explanation: According to the UiPath documentation, Page Units are the measure used to
license Document Understanding products. Page Units are charged based on the number
of pages processed by the Document Understanding models, such as extractors, OCR
engines, and classifiers. Therefore, applying OCR on a 10-page document consumes Page
Units, while the other options do not. The creation of a Document Validation Action in
Action Center does not consume any Page Units, as it is a human-in-the-loop activity.
Using ML Classifier on a 21-page document does not consume Page Units, as it is a free
model. Using Intelligent Form Extractor on a 5-page document with 0 successful
extractions does not consume Page Units, as the extractor only charges for successful
extractions.
References:
AI Center - AI Units
Document Understanding - Metering & Charging LogicC. Using ML Classifier on a 21-page document.
D. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.
Answer: A
Explanation: According to the UiPath documentation, Page Units are the measure used to
license Document Understanding products. Page Units are charged based on the number
of pages processed by the Document Understanding models, such as extractors, OCR
engines, and classifiers. Therefore, applying OCR on a 10-page document consumes Page
Units, while the other options do not. The creation of a Document Validation Action in
Action Center does not consume any Page Units, as it is a human-in-the-loop activity.
Using ML Classifier on a 21-page document does not consume Page Units, as it is a free
model. Using Intelligent Form Extractor on a 5-page document with 0 successful
extractions does not consume Page Units, as the extractor only charges for successful
