Practice Free UiPath-SAIv1 Exam Online Questions
Question #31
Which of the following consumes Page Units?
- 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.
Correct Answer: A
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 Logic
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 Logic
Question #32
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 into one and only one category or object.
- C . To organize knowledge by using a controlled vocabulary to make it easier to find related information.
- D . To apply relationship schemas other than parent-child hierarchies, such as network structures on the processed data.
Correct Answer: A
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
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 #33
Which UiPath Communications Mining model performance factor assesses the proportion of the entire dataset that has informative label predictions?
- A . Average label performance.
- B . Coverage.
- C . Balance.
- D . Underperforming labels.
Correct Answer: B
B
Explanation:
According to the UiPath Communications Mining documentation, coverage is one of the four main factors that contribute to the model rating, which is a holistic measure of the model’s performance and health. Coverage assesses the proportion of the entire dataset that has informative label predictions, meaning that the predicted labels are not generic or irrelevant. Coverage is calculated as the percentage of verbatims (communication units) that have at least one informative label out of the total number of verbatims in the dataset. A high coverage indicates that the model is able to capture the main topics and intents of the communications, while a low coverage suggests that the model is missing important information or producing noisy predictions.
References:
Communications Mining – Understanding and improving model performance Communications Mining – Model Rating
Communications Mining – It’s All in the Numbers – Assessing Model Performance with Metrics
B
Explanation:
According to the UiPath Communications Mining documentation, coverage is one of the four main factors that contribute to the model rating, which is a holistic measure of the model’s performance and health. Coverage assesses the proportion of the entire dataset that has informative label predictions, meaning that the predicted labels are not generic or irrelevant. Coverage is calculated as the percentage of verbatims (communication units) that have at least one informative label out of the total number of verbatims in the dataset. A high coverage indicates that the model is able to capture the main topics and intents of the communications, while a low coverage suggests that the model is missing important information or producing noisy predictions.
References:
Communications Mining – Understanding and improving model performance Communications Mining – Model Rating
Communications Mining – It’s All in the Numbers – Assessing Model Performance with Metrics
Question #34
Which of the following business processes is the most suitable for automation?
- A . Suggesting key financial client’s concern over changes to the stock market.
- B . Setting goals and objectives for a company.
- C . Scheduling and sending patient reminders in a healthcare center.
- D . Creating a presentation using various sources based on different topics.
Correct Answer: C
C
Explanation:
The process of scheduling and sending patient reminders is repetitive, rule-based, and time-sensitive, making it ideal for automation. The other options involve subjective judgment, creativity, or require human intervention.
Reference: UiPath Business Process Automation
C
Explanation:
The process of scheduling and sending patient reminders is repetitive, rule-based, and time-sensitive, making it ideal for automation. The other options involve subjective judgment, creativity, or require human intervention.
Reference: UiPath Business Process Automation
Question #35
What is the definition of Machine Learning?
- A . An area of machine learning concerned with artificial neural networks. These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.
- B . The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
- C . A sub-field of artificial intelligence that enables systems to learn from data. Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.
- D . A branch of artificial intelligence that deals with analyzing, understanding, and generating human natural languages. For example, NLP enables computers to hear speech, read text, interpret the text/speech or measure the sentiment.
Correct Answer: C
C
Explanation:
Machine Learning is a sub-field of artificial intelligence that allows systems to learn from data and improve over time without being explicitly programmed. It leverages algorithms to deduce and predict outcomes based on prior experiences and information.
Reference: UiPath Machine Learning Concepts
C
Explanation:
Machine Learning is a sub-field of artificial intelligence that allows systems to learn from data and improve over time without being explicitly programmed. It leverages algorithms to deduce and predict outcomes based on prior experiences and information.
Reference: UiPath Machine Learning Concepts
Question #36
What components are part of the Document Understanding Process template?
- A . Load Taxonomy, Digitization, Categorization, Data Validation, and Export
- B . Load Document, Categorization, Data Extraction, and Validation
- C . Load Taxonomy, Digitization, Classification, Data Extraction, and Data Validation Export
- D . Import, Classification, Text Extractor, and Data Validation
Correct Answer: C
C
Explanation:
The Document Understanding Process template includes key phases: Load Taxonomy, Digitization, Classification, Data Extraction, and Data Validation Export. These steps ensure end-to-end document processing.
Reference: UiPath Document Understanding
C
Explanation:
The Document Understanding Process template includes key phases: Load Taxonomy, Digitization, Classification, Data Extraction, and Data Validation Export. These steps ensure end-to-end document processing.
Reference: UiPath Document Understanding
Question #37
What does Data Extraction do?
- A . Identifies and extracts specific information that should be processed.
- B . Applies rules for validating that the information extracted from a document is correct.
- C . Digitizes the document that should be processed.
- D . Identifies words and their coordinates from images and PDFs.
Correct Answer: A
A
Explanation:
Data Extraction identifies and extracts specific information from documents to be processed by
automations. This is a key part of UiPath’s Document Understanding Framework, enabling bots to process structured and unstructured documents effectively.
Reference: UiPath Document Understanding – Data Extraction
A
Explanation:
Data Extraction identifies and extracts specific information from documents to be processed by
automations. This is a key part of UiPath’s Document Understanding Framework, enabling bots to process structured and unstructured documents effectively.
Reference: UiPath Document Understanding – Data Extraction
Question #38
What are the out-of-the-box packages types available in Al Center?
- A . Pre-trained. fine-tunable, and reviewed.
- B . Pre-trained. custom training, and fine-tunable.
- C . Custom training, fine-tunable, and reviewed.
- D . Pre-trained, custom training, and reviewed.
Correct Answer: B
B
Explanation:
UiPath AI Center offers three primary package types: pre-trained, custom training, and fine-tunable models. These are essential for various use cases, from leveraging existing models to training custom ones based on specific data
B
Explanation:
UiPath AI Center offers three primary package types: pre-trained, custom training, and fine-tunable models. These are essential for various use cases, from leveraging existing models to training custom ones based on specific data
Question #39
In UiPath Communications Mining, what does the Reports section contain?
- A . Tools for evaluating model performance.
- B . Tools for evaluating label and entity performance.
- C . Tools for comparing different model versions.
- D . Tools for message analytics and monitoring.
Correct Answer: D
D
Explanation:
In UiPath Communications Mining, the Reports section provides a variety of tools for analyzing and monitoring messages within a dataset. This includes functionalities like label summaries, trends, and message segmentation, which allow users to gain insights into message volumes, sentiment trends, and other analytical metrics. The Reports section is integral for tracking the performance of messages and the overall dataset, making it useful for monitoring communication channels
D
Explanation:
In UiPath Communications Mining, the Reports section provides a variety of tools for analyzing and monitoring messages within a dataset. This includes functionalities like label summaries, trends, and message segmentation, which allow users to gain insights into message volumes, sentiment trends, and other analytical metrics. The Reports section is integral for tracking the performance of messages and the overall dataset, making it useful for monitoring communication channels
Question #40
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 generate a 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 System Exception, and the post processing/cleaning if required.
- C . End Process in the Document Understanding Process silently shuts down the Virtual Machine so that another robot can use it.
- D . End Process is a feature in the Document Understanding Process that exports the extracted data into a readable document format.
Correct Answer: B
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
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