How to pass UiPath UiPath-SAIv1 exam with the help of dumps?
DumpsPool provides you the finest quality resources you’ve been looking for to no avail. So, it's due time you stop stressing and get ready for the exam. Our Online Test Engine provides you with the guidance you need to pass the certification exam. We guarantee top-grade results because we know we’ve covered each topic in a precise and understandable manner. Our expert team prepared the latest UiPath UiPath-SAIv1 Dumps to satisfy your need for training. Plus, they are in two different formats: Dumps PDF and Online Test Engine.
How Do I Know UiPath UiPath-SAIv1 Dumps are Worth it?
Did we mention our latest UiPath-SAIv1 Dumps PDF is also available as Online Test Engine? And that’s just the point where things start to take root. Of all the amazing features you are offered here at DumpsPool, the money-back guarantee has to be the best one. Now that you know you don’t have to worry about the payments. Let us explore all other reasons you would want to buy from us. Other than affordable Real Exam Dumps, you are offered three-month free updates.
You can easily scroll through our large catalog of certification exams. And, pick any exam to start your training. That’s right, DumpsPool isn’t limited to just UiPath Exams. We trust our customers need the support of an authentic and reliable resource. So, we made sure there is never any outdated content in our study resources. Our expert team makes sure everything is up to the mark by keeping an eye on every single update. Our main concern and focus are that you understand the real exam format. So, you can pass the exam in an easier way!
IT Students Are Using our UiPath Certified Professional Specialized AI Professional v1.0 Dumps Worldwide!
It is a well-established fact that certification exams can’t be conquered without some help from experts. The point of using UiPath Certified Professional Specialized AI Professional v1.0 Practice Question Answers is exactly that. You are constantly surrounded by IT experts who’ve been through you are about to and know better. The 24/7 customer service of DumpsPool ensures you are in touch with these experts whenever needed. Our 100% success rate and validity around the world, make us the most trusted resource candidates use. The updated Dumps PDF helps you pass the exam on the first attempt. And, with the money-back guarantee, you feel safe buying from us. You can claim your return on not passing the exam.
How to Get UiPath-SAIv1 Real Exam Dumps?
Getting access to the real exam dumps is as easy as pressing a button, literally! There are various resources available online, but the majority of them sell scams or copied content. So, if you are going to attempt the UiPath-SAIv1 exam, you need to be sure you are buying the right kind of Dumps. All the Dumps PDF available on DumpsPool are as unique and the latest as they can be. Plus, our Practice Question Answers are tested and approved by professionals. Making it the top authentic resource available on the internet. Our expert has made sure the Online Test Engine is free from outdated & fake content, repeated questions, and false plus indefinite information, etc. We make every penny count, and you leave our platform fully satisfied!
Frequently Asked Questions
UiPath UiPath-SAIv1 Sample Question Answers
Question # 1
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
Question # 3
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
Question # 5
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
Question # 6
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
Question # 7
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]
Question # 10
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
Question # 11
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
What does the Label Trends table in UiPath Communications Mining show?
A. How the top 10 labels for a given time period perform compared to the previous periodand their change in rank. B. How the top 10 senders for a given time period perform compared to the previous periodand their change in rank. C. How the top 10 entities for a given time period perform compared to the previous periodand their change in rank. D. How the top 10 labels and entities for a given time period perform compared to theprevious period and their change in rank.
Answer: A
Explanation: The Label Trends table in UiPath Communications Mining shows the trend of
the top 10 highest volume labels over the selected time period, as well as their percentage
change and rank change compared to the previous period1. The table allows users to
quickly identify which labels are increasing or decreasing in volume, and by how much,
over time. The table also shows the net sentiment score for each label, which is calculated as the difference between the positive and negative sentiment probabilities for each
verbatim2. The table can be filtered by data type, source, date range, and label
category. Users can also sort the table by label name, volume, percentage change, rank
change, or net sentiment1.
References: 1: Trends 2: Sentiment Analysis
Question # 14
When creating a training dataset, what is the recommended number of samples for theClassification fields?
A. 5-10 document samples from each class. B. 10-20 document samples from each class. C. 20-50 document samples from each class. D. 50-200 document samples from each class.
Answer: C
Explanation: According to the UiPath documentation, the recommended number of
samples for the classification fields depends on the number of document types and layouts
that you want to classify. The more document types and layouts you have, the more
samples you need to cover the diversity of your data. However, a general guideline is to
have at least 20-50 document samples from each class, as this would provide enough data
for the classifiers to learn from12. A large number of samples per layout is not mandatory,
as the classifiers can generalize from other layouts as well3.
References: 1: Document Classification Training Overview 2: Document Classification
Training Related Activities 3: Training High Performing Models
Question # 15
What are the languages supported by the generic Document Understanding ML Package?
A. Languages using the Greek left-to-right alphabet. Japanese, and Chinese. B. Languages using the Cyrillic alphabet, the Greek left-to-right alphabet, and Chinese. C. Languages using the Latin alphabet (like Italian, French. Portuguese. Spanish, andRomanian), and the Greek left-to-right alphabet. D. Languages using the Latin alphabet, the Cyrillic alphabet, the Greek left-to-rightalphabet. Japanese, and Chinese.
Answer: D
Explanation: According to the UiPath documentation1, the generic Document
Understanding ML Package supports data extraction from any type of structured or semistructured
documents, building an ML model from scratch. The supported languages for
this package are Latin-based languages, Cyrillic languages, Greek left-to-right, and
Japanese (Preview). Additionally, the documentation23 also mentions that the package can
support Chinese with the use of an OCR that supports that language. Therefore, the
ML Packages, Document Understanding - ML Packages.
Question # 16
In which of the following scenarios, the ML Classifier is the only recommended classifier tobe used, according to best practice?
A. When the custom document types are very similar and file splitting is not necessary. B. When the custom document types are not similar and file splitting is not necessary. C. When the custom document types are not similar and file splitting is necessary. D. When the custom document types are very similar and file splitting is necessary.
Answer: A
Explanation: The ML Classifier is a document classifier that uses a machine learning
model deployed as an ML Skill in AI Center to perform document classification tasks. The
ML Classifier can work by default with Invoices, Purchase Orders, Receipts, and Utility
Bills, or with custom document types that are trained using the Data Manager and the
Machine Learning Classifier Trainer12.
According to the best practice, the ML Classifier is the only recommended classifier to be used when the custom document types are very similar and file splitting is not necessary.
This is because the ML Classifier can handle complex and ambiguous cases where the
document types are hard to distinguish by rules or keywords, and can also learn from
feedback and improve over time. File splitting is not necessary when the documents are
single-page or have a consistent number of pages per document type3.
The other options are not correct because they are scenarios where other classifiers, such
as the Keyword Based Classifier or the Intelligent Keyword Classifier, can be used in
combination with the ML Classifier or instead of it. These classifiers are based on rules or
keywords that can identify the document types based on their content or metadata, and can
also perform file splitting if the documents are multi-page or have a variable number of
What are the mandatory activities to be included in an automation workflow to allow aremote knowledge worker to pick up an action that validates the extracted data in the formof a Document Validation Action?
A. Present Validation Station, Wait for Document Validation Action and Resume. B. Orchestration Process Activities. C. Document Understanding Process Activities. D. Create Document Validation Action, Wait for Document Validation Action and Resume.
Answer: D
Explanation: To enable a remote knowledge worker to validate the extracted data from
documents in Action Center, the automation workflow needs to include the following
activities12:
Create Document Validation Action: This activity creates an action of type
Document Validation in Orchestrator Action Center, and returns an action object
as output. The action object contains the information needed to resume the
workflow after the human validation is completed. The input properties of this
activity include the action details, such as title, priority, catalog, and folder, and the
document validation data, such as the document object model, the document text,
the taxonomy, and the automatic extraction results.
Wait for Document Validation Action and Resume: This activity suspends the
execution of the workflow until the human validation is done in Action Center, and
then resumes it with the updated extraction results. The input property of this
activity is the action object obtained from the Create Document Validation Action
activity. The output property is the validated extraction results, which can be used
Which of the following extractors can be used for Data Extraction Scope activity?
A. Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and RegexBased Extractor. B. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex BasedExtractor. C. Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent FormExtractor D. Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and MachineLearning Extractor.
Answer: D
Explanation: The Data Extraction Scope activity provides a scope for extractor activities,
enabling you to configure them according to the document types defined in your taxonomy.
The output of the activity is stored in an ExtractionResult variable, containing all
automatically extracted data, and can be used as input for the Export Extraction Results
activity. This activity also features a Configure Extractors wizard, which lets you specify
exactly what fields from the document types defined in the taxonomy you want to extract1.
The extractors that can be used for Data Extraction Scope activity are:
Regex Based Extractor: This extractor enables you to use regular expressions to
extract data from text documents. You can define your own expressions or use the
predefined ones from the Regex Based Extractor Configuration wizard2.
Form Extractor: This extractor enables you to extract data from semi-structured
documents, such as invoices, receipts, or purchase orders, based on the position
and relative distance of the fields. You can define the templates for each document
type using the Form Extractor Configuration wizard3.
Intelligent Form Extractor: This extractor enables you to extract data from semistructured
documents, such as invoices, receipts, or purchase orders, based on
the labels and values of the fields. You can define the fields for each document
type using the Intelligent Form Extractor Configuration wizard.
Machine Learning Extractor: This extractor enables you to extract data from any
type of document, using a machine learning model that is trained on your data.
You can use the predefined models from UiPath or your own custom models
hosted on AI Center or other platforms. You can configure the fields and the model
for each document type using the Machine Learning Extractor Configuration
wizard.
References: 1: Data Extraction Scope 2: Regex Based Extractor 3: Form Extractor :
Intelligent Form Extractor : Machine Learning Extractor
Question # 19
What information does the comparison between two cohorts display on the Comparisonpage in UiPath Communications Mining?
A. Total verbatim count and proportion for each label. B. Entity count for each metadata. C. Verbatim content for each label. D. Differences in verbatim length between Group A and Group B.
Answer: A
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.
How long does the typical Machine Learning model deployment process take in UiPath AICenter?
A. Less than 5 minutes. B. Between 5 and 10 minutes. C. Between 10 and 15 minutes. D. More than 15 minutes.
Answer: C
Explanation: The typical machine learning model deployment process in UiPath AI Center
usually takes between 10-15 minutes1. This process involves wrapping the model in
UiPath’s serving framework and deploying it within a namespace on AI Fabric’s Kubernetes
cluster that is only accessible by your tenant1. Please note that the actual time may vary depending on the complexity of the model and other factors.
AI Center - Managing ML Skills (uipath.com)
Question # 21
What components are part of the Document Understanding Process template?
A. Import. Classification. Text Extractor, and Data Validation. B. Load Document. Categorization. Data Extraction, and Validation. C. Load Taxonomy, Digitization. Classification, Data Extraction, and Data ValidationExport. D. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.
Answer: C
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
Question # 22
What are the available options for Scoring in Document Manager, that apply only to stringcontent type?
A. Exact match and Naive string search. B. Exact match and Phonetic matching. C. Exact match and Levenshtein. D. Exact match and Finite state automation-based search.
Answer: C
Explanation: According to the UiPath documentation, the available options for Scoring in
Document Manager, that apply only to string content type, are exact match and
Levenshtein. Exact match is a scoring strategy that considers a prediction to be correct
only if it exactly matches the true value. Levenshtein is a scoring strategy that measures
the similarity between two strings by counting the minimum number of edits (insertions,
deletions, or substitutions) required to transform one string into another. The lower the
Levenshtein distance, the higher the score. These options can be configured in the Advanced tab of the Edit Field window for string fields.
References:
Document Understanding - Create and Configure Fields
Document Understanding - Training High Performing Models
Question # 23
How can you build custom models supported by AI Center?
A. Using the Al Center IDE (Integrated Development Environment). B. Using the Al Center model builder. C. Using a Python IDE (Integrated Development Environment) or an AutoML platform. D. Using a C/C++ IDE (Integrated Development Environment), then upload the code to AlCenter IDE.
Answer: C
Explanation: To build custom models supported by AI Center, you can use a Python IDE
or an AutoML platform of your choice. A Python IDE is a software application that provides
tools and features for writing, editing, debugging, and running Python code. An AutoML
platform is a service that automates the process of building and deploying machine
learning models, such as data preprocessing, feature engineering, model selection,
hyperparameter tuning, and model evaluation. Some examples of Python IDEs are
PyCharm, Visual Studio Code, and Jupyter Notebook. Some examples of AutoML
platforms are Google Cloud AutoML, Microsoft Azure Machine Learning, and DataRobot.
To use a Python IDE, you need to install the required Python packages and dependencies,
write the code for your model, and test it locally. Then, you need to package your model as
a zip file that follows the AI Center ML Package structure and requirements. You can then
upload the zip file to AI Center and create an ML Skill to deploy and consume your model.
To use an AutoML platform, you need to sign up for the service, upload your data,
configure your model settings, and train your model. Then, you need to export your model
as a zip file that follows the AI Center ML Package structure and requirements. You can
then upload the zip file to AI Center and create an ML Skill to deploy and consume your
model.
References: AI Center - Building ML Packages, AI Center - ML Package Structure, AI
Center - Creating ML Skills
Question # 24
For an analytics use case, what are the recommended minimum model performancerequirements in UiPath Communications Mining?
A. Model Ratings of "Good" or better and individual performance factors rated as "Good" orbetter. B. Model Ratings of "Good" and individual performance factors rated as "Excellent". C. Model Ratings of "Excellent" and individual performance factors rated as "Good" orbetter. D. Model Ratings of "Excellent" and individual performance factors rated as "Excellent".
Answer: A
Question # 25
When dealing with variable-length data, or data spanning over multiple pages of thedocument (e.g. item tables), what is the recommended data extraction methodology to beused?
A. Hybrid data extraction. B. Rule-based data extraction. C. Model-based data extraction. D. Manual data extraction.
Answer: C
Explanation: Model-based data extraction, often involving machine learning models, is
particularly effective for handling complex data structures such as variable-length data or
data that spans multiple pages. This approach adapts better to varying formats and can
extract information more accurately in such scenarios compared to rule-based or manual
methods.
Question # 26
What is the purpose of the End Process in the Document Understanding Process?
A. The purpose of the End Process in the Document Understanding Process is to generatea summary report of the processing statistics and performance metrics. B. End Process sets the queue transaction status as Successful in case of no exception,and as Failed in case of an exception with their corresponding Business or SystemException, and the post processing/cleaning if required. C. End Process in the Document Understanding Process silently shuts down the VirtualMachine so that another robot can use it. D. End Process is a feature in the Document Understanding Process that exports theextracted data into a readable document format.
Answer: B
Explanation: The End Process is the final stage of the Document Understanding Process,
which is a fully functional UiPath Studio project template based on a document processing
flowchart. The End Process is responsible for setting the queue transaction status, logging
the results, and performing any post processing or cleaning actions if needed. The End
Process sets the queue transaction status as Successful if the document was processed
without any exception, and as Failed if an exception occurred, either a Business Exception
(such as invalid data) or a System Exception (such as network failure). The End Process
also adds the extracted data and the validation status as output arguments to the queue
transaction. The End Process also logs the processing statistics, such as the number of
documents processed, the number of exceptions, the average processing time, and the
accuracy rate. The End Process also performs any post processing or cleaning actions,
such as deleting temporary files, closing applications, or sending notifications1.
References: 1: Document Understanding Process: Studio Template
Question # 27
How is the Taxonomy component used in the Document Understanding Template?
A. To define the document types and the pieces of information targeted for data extraction(fields) for each document type. B. To apply rigor in the taxonomy of data, ensuring any newly discovered object fits intoone and only one category or object. C. To organize knowledge by using a controlled vocabulary to make it easier to find relatedinformation. D. To apply relationship schemas other than parent-child hierarchies, such as networkstructures on the processed data.
Answer: A
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
Question # 28
What are the out-of-the-box model types available in AI Center?
A. Pre-trained, custom training, and reviewed. B. Custom training, fine-tunable, and reviewed. C. Pre-trained, fine-tunable, and reviewed. D. D. Pre-trained, custom training, and fine-tunable.
Answer: D
Explanation: According to the UiPath documentation, AI Center provides three types of
out-of-the-box model types that can be used for different purposes and scenarios1:
Pre-trained: These are models that are already trained on a large and diverse
dataset and can be used as-is for inference or prediction. They do not require any
additional data or training from the user. Examples of pre-trained models are
Language Detection, Sentiment Analysis, and Question Answering.
Custom training: These are models that allow the user to train them on their own
data using the AI Center UI or API. They require the user to upload a dataset,
configure the training parameters, and monitor the training progress and results.
Examples of custom training models are Image Classification, Text Classification,
and Named Entity Recognition.
Fine-tunable: These are models that are pre-trained on a general dataset but can
be further trained or fine-tuned on a specific dataset provided by the user. They
offer the best of both worlds: the benefit of using a pre-trained model and the
flexibility of customizing it to the user’s needs. Examples of fine-tunable models
are Object Detection, Text Summarization, and Language Translation.
References:
1: Out-of-the-Box Packages
Question # 29
What is the difference between the Document Understanding Process and the DocumentUnderstanding Framework?
A. The Document Understanding Framework contains the activities that can be used in aLibrary, while the Document Understanding Process is the template that can be found in Studio. B. The Document Understanding Framework contains the activities that can be used in aProcess, while the Document Understanding Process is the template that can be found inStudio. C. The Document Understanding Process contains the activities that can be used in aLibrary, while the Document Understanding Framework is the template that can be found inStudio. D. The Document Understanding Process contains the activities that can be used in aProcess, while the Document Understanding Framework is the template that can be foundin Studio.
Answer: D
Explanation: According to the UiPath documentation portal1, the Document
Understanding Process 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 Document Understanding Process is preconfigured with a series of basic
document types in a taxonomy, a classifier configured to distinguish between these
classes, and extractors to showcase how to use the Data Extraction capabilities of the
framework. It is meant to be used as a best practice example that can be adapted to your
needs while displaying how to configure each of its components1. The Document
Understanding Framework, on the other hand, is a set of activities that can be used to build
custom document processing workflows. The framework facilitates the processing of
incoming files, from file digitization to extracted data validation, all in an open, extensible,
and versatile environment. The framework enables you to combine different approaches to
extract information from multiple document types. The framework consists of several
components, such as Taxonomy, Digitization, Classification, Data Extraction, Data
Validation, and Data Consumption2. Therefore, option D is the correct answer, as it
describes the difference between the Document Understanding Process and the Document
Understanding Framework.
References: 1 Document Understanding Process: Studio Template 2 Document
Understanding - Introduc
Question # 30
What is the page unit cost per extracted page for the RegEx Extractor?
A. 0 B. 0.2 C. 0.5 D. 1
Answer: A
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.
Which is the correct description of the Configure Extractors Wizard?
A. A mandatory step in the extractor configuration that allows choosing which extractorsare applied to each field. B. A mandatory step in the extractor configuration that allows choosing which extractorsare applied to each document type and field. C. A mandatory step in the extractor configuration that allows choosing which extractorsare applied to each document type. D. An optional step in the extractor configuration which allows choosing which extractors are applied to each document type.
Answer: B
Explanation: The Configure Extractors Wizard is a tool that enables you to select and
customize the extractors that are used for data extraction from documents. It is accessed
via the Data Extraction Scope activity, which is a container for extractor activities. The
wizard allows you to map the fields defined in your taxonomy with the fields supported by
each extractor, and to set the minimum confidence level and the framework alias for each
extractor. The wizard is mandatory for the extractor configuration, as it ensures that the
extractors are applied correctly to each document type and field1.
References: Configure Extractors Wizard of Data Extraction Scope
Question # 32
A Document Understanding Process is in production. According to best practices, what arethe locations recommended for exporting the result files?
A. Network Attached Storage and Orchestrator Bucket. B. Locally, Temp Folder, Network Attached Storage, and Orchestrator Bucket. C. Orchestrator Bucket and Queue Item. D. On a VM, Orchestrator Bucket, and Network Attached Storage.
Answer: A
Explanation: In a Document Understanding Process, particularly when it is in production, it
is crucial to manage output data securely and efficiently. Utilizing Network Attached
Storage (NAS) and Orchestrator Buckets are recommended practices for exporting result
files for several reasons:
Network Attached Storage (NAS): NAS is a dedicated file storage that allows
multiple users and client devices to retrieve data from centralized disk capacity.
Using NAS in a production environment for storing result files is beneficial due to
its accessibility, capacity, and security features. It facilitates easy access and
sharing of files within a network while maintaining data security.
Orchestrator Bucket: Orchestrator Buckets in UiPath are used for storing files that
can be easily accessed by the robots. This is particularly useful in a production
environment because it provides a centralized, cloud-based storage solution that is
scalable, secure, and accessible from anywhere. This aligns with the best
practices of maintaining high availability and security for business-critical data.
The other options (B, C, and D) include locations that might not be as secure or efficient for
a production environment. For example, storing files locally or in a temp folder can pose
security risks and is not scalable for large or distributed systems. Similarly, storing directly
on a VM might not be the most efficient or secure method, especially when dealing with
sensitive data.
Question # 33
Which role consumes ML Skills within customized workflows in Studio using the ML Skillactivity from the UiPath.MLServices.Activities package?
A. Data Scientist. B. Administrator. C. RPA Developer. D Process Controller
Answer: C
Explanation: According to the UiPath documentation portal1, the RPA Developer is the
role that consumes ML Skills within customized workflows in Studio using the ML Skill
activity from the UiPath.MLServices.Activities package. The RPA Developer is responsible
for designing, developing, testing, and deploying automation workflows using UiPath Studio
and other UiPath products. The RPA Developer can use the ML Skill activity to retrieve and
call all ML Skills available on the AI Center service and request them within the automation
workflows. The ML Skill activity allows the RPA Developer to pass data to the input of the
skill, test the skill, and receive the output of the skill as JSON response, status code, and
headers2. Therefore, option C is the correct answer, as it describes the role and the activity
that are related to consuming ML Skills in Studio. Option A is incorrect, as the Data
Scientist is the role that creates and trains ML models using AI Center or other tools, and
publishes them as ML Packages or OS Packages1. Option B is incorrect, as the
Administrator is the role that manages the AI Center service, such as configuring the
infrastructure, setting up the permissions, and monitoring the usage and
performance1. Option D is incorrect, as the Process Controller is the role that deploys ML
Packages or OS Packages as ML Skills, and manages the versions, the endpoints, and the
API keys of the skills1.
References: 1 AI Center - User Personas 2 Activities - ML Skill
Question # 34
Which of the following time periods can be selected when viewing Trends in UiPathCommunications Mining?Which of the following time periods can be selected when viewing Trends in UiPathCommunications Mining?
A. Daily, Monthly, Quarterly, Yearly. B. Daily, Weekly, Monthly, Yearly. C. Daily, Bi-weekly, Monthly, Yearly. D. Daily, Bi-weekly, Quarterly, Yearly.
Answer: B
Explanation: According to the UiPath Communications Mining documentation, the Trends
tab in the Reports page displays charts for verbatim volume, label volume, and sentiment
over a selected time period. Users can choose the time period for the data in the filter bar,
and the time sequencing of the chart (i.e. daily, weekly, etc.) in the top right dropdown menu. The available options for the time sequencing are Daily, Weekly, Monthly, and
Yearly. These options allow users to see how the trends change over different time
intervals and identify patterns or anomalies.
References:
Communications Mining - Using Reports
Communications Mining - Trends
Question # 35
What should a UiPath Communications Mining taxonomy contain when it is beingimported?
A. Label predictions. B. Entity descriptions. C. Entity predictions. D. Label descriptions.
Answer: D
Explanation: According to the UiPath documentation, a UiPath Communications Mining
taxonomy is a collection of all the labels applied to the verbatims in a dataset, structured in
a hierarchical manner. Labels are the concepts and intents that you want to capture in the
dataset to suit your specific objectives. When you import your taxonomy from a
spreadsheet, you need to provide the label descriptions, which are the names of the labels
and their level in the hierarchy. Label predictions and entity predictions are the outputs of
the model training process, and they are not part of the taxonomy. Entity descriptions are
the definitions of the entities that you want to extract from the verbatims, and they are not
part of the taxonomy either.
References:
Communications Mining - Taxonomies
Communications Mining - Importing your taxonomy
Communications Mining - Building your taxonomy structure
Question # 36
Which activity should be used for classification validation in attended mode?
A. Create Document Classification Action. B. Train Classifiers Scope. C. Wait for Document Classification and Resume. D. Present Classification Station.
Answer: D
Explanation: The Present Classification Station activity is used for classification validation
in attended mode. It allows the user to review and correct the classification results of
documents using the Classification Station interface. The other options are not suitable for
attended mode, as they are either used for creating Action Center tasks (A and C) or for
training classifiers (B).
References: Classification Station - UiPath Documentation Portal, Document Classification
What are the two main data extraction methodologies used in document understandingprocesses?
A. Hybrid and manual data extraction. B. Rule-based and model-based data extraction. C. Rule-based and hybrid data extraction. D. Manual and model-based data extraction.
Answer: B
Explanation: According to the UiPath documentation, there are two common types of data
extraction methodologies used in document understanding processes: rule-based data
extraction and model-based data extraction12. Rule-based data extraction targets
structured documents, such as forms, invoices, or receipts, that have a fixed layout and a
predefined set of fields. Rule-based data extraction uses predefined rules, such as regular
expressions, keywords, or coordinates, to locate and extract the relevant data from the
documents1. Model-based data extraction is used to process semi-structured and
unstructured documents, such as contracts, emails, or reports, that have a variable layout
and a diverse set of fields. Model-based data extraction uses machine learning models,
such as neural networks, to learn from examples and extract the relevant data from the
documents1. Both methodologies have their advantages and limitations, and depending on
the use case, they can be used separately or in combination, in a hybrid approach2.
References: 1: Data Extraction Overview 2: Document Processing with Improved Data
Extraction
Question # 38
Which is a high-level view of the tabs within an AI Center project?
A. Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs. B. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and MLLogs. C. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills. D. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
Answer: D
Explanation: A high-level view of the tabs within an AI Center project is as follows:
Dashboard: This tab provides an overview of the project’s status, such as the
number of datasets, pipelines, packages, skills, and logs, as well as the AI Units
consumption and quota.
Datasets: This tab enables you to upload, view, and manage the datasets that are
used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub-folders1.
Data Labeling: This tab enables you to upload raw data, annotate text data in the
labeling tool (for classification or entity recognition), and use the labeled data to
train ML models. It is also used by the human reviewer to re-label incorrect
predictions as part of the feedback process2.
ML Packages: This tab enables you to upload, view, and manage the ML
packages and package versions within the project. An ML package is a group of
package versions of the same package type, and a package version is a trained
model that can be deployed to a skill3.
Pipelines: This tab enables you to create, view, and manage the pipelines and
pipeline runs within the project. A pipeline is a description of an ML workflow,
including the functions and their order of execution, and a pipeline run is an
execution of a pipeline based on code provided by the user4.
ML Skills: This tab enables you to deploy, view, and manage the ML skills within
the project. An ML skill is a live deployment of a package version, which can be
consumed by an RPA workflow using an ML skill activity in UiPath Studio5.
ML Logs: This tab enables you to view and filter the logs related to the project,
such as the events, messages, and errors that occurred during the pipeline runs,
skill deployments, and skill executions6.
References:
1: About Datasets 2: About Data Labeling 3: About ML Packages 4: About
Pipelines 5: About ML Skills 6: About ML Logs
Question # 39
What does the Automation Suite installer enable?
A. Enables the deployment, management, and improvement of ML models on UiPathAutomation Cloud, and requires no infrastructure and no maintenance. B. Enables the deployment, management, and improvement of ML models locally, andrequires manual download of all the resources and then loading them into the node. C. Enables the deployment of the full UiPath Automation Platform in the environment of choice and contains everything in one package that can be deployed in multi-node modewith automatic scaling and built-in HA. monitor, configure, and upgrade. D. Enables the deployment, management, and improvement of ML models locally witheasy installation due to the automatic retrieval of the installer and associated artifacts fromthe internet.
Answer: C
Explanation: According to the UiPath documentation portal1, the Automation Suite
installer is a single package that enables the deployment of the full UiPath Automation
Platform in the environment of choice, whether on-premises or in the cloud. The
Automation Suite installer contains all the components and dependencies required for the
installation, such as the infrastructure, the orchestrator, the AI Center, the Action Center,
the Insights, the Test Suite, and the Document Understanding. The Automation Suite
installer also supports multi-node deployment with automatic scaling and built-in high
availability, as well as easy monitoring, configuration, and upgrade options1. Therefore,
option C is the correct answer, as it describes the features and benefits of the Automation
Suite installer. Option A is incorrect, as it refers to the UiPath Automation Cloud, which is a
different offering that does not require an installer. Option B is incorrect, as it describes a
manual installation process that is not enabled by the Automation Suite installer. Option D
is incorrect, as it confuses the Automation Suite installer with the installUiPathAS.sh script,
which is a separate file that is used to launch the installer wizard2.
References: 1 Automation Suite - Overview 2 Automation Suite - Downloading the
installation packages
Question # 40
Which of the following statements is true regarding reviewing and applying entities inUiPath Communications Mining?
A. A single entity value can be split across multiple paragraphs. B. If the entity value is correctly predicted, but the entity type is wrong, it cannot bechanged. C. All of the entities within a paragraph should be reviewed. D. All of the entities in a communication must be reviewed.
Answer: C
Explanation: According to the UiPath Communications Mining documentation, reviewing
and applying entities is a crucial step for improving the accuracy and performance of the
entity extraction models. When reviewing entities, users should check all of the predicted
entities within a paragraph, as well as any missing or incorrect ones. Users can accept,
reject, edit, or create entities using the platform’s interface or keyboard shortcuts. Users
can also change the entity type if the value is correct but the type is wrong. Reviewing and
applying entities helps the platform learn from the user feedback and refine its predictions
over time. It also helps users assess the automation potential and benefit of the
communications data.
References:
Communications Mining - Reviewing and applying entities
Under what condition can a dataset be edited in UiPath AI Center?
A. If it is not being used in any active pipeline. B. If it has not been modified within the last 24 hours. C. There are no restrictions in editing a dataset. D. If it is not linked to any data labeling session.
Answer: A
Explanation: According to the UiPath documentation, a dataset is a folder of storage
containing arbitrary sub-folders and files that allows machine learning models in your
project to access new data points. You can edit a dataset’s name, description, or content
from the Datasets > [Dataset Name] page, by clicking Edit dataset. However, you can only
edit a dataset if it is not currently being used in an active pipeline. A pipeline is a sequence
of steps that defines how to train, test, and deploy a machine learning model. If a dataset is
being used in an active pipeline, you will see a lock icon next to it, indicating that it cannot
be edited. You can either wait for the pipeline to finish or stop it before editing the dataset.
References: AI Center - Managing Datasets
AI Center - About Datasets
AI Center - About Pipelines
Question # 42
What is the role of the Taxonomy Manager?
A. To select which extractors are trained for each document type and field. B. To create and edit a Taxonomy file specific to the current automation project. C. To select the type of ML that can be used in the project. D. To present a document processing specific user interface for validating and correctingautomatic classification outputs.
Answer: B
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
Leave a comment
Your email address will not be published. Required fields are marked *