UiPath UiSAIv1 Exam Dumps & Practice Test Questions
Question 1
What is the main function of a pre-built Custom Named Entity Recognition (NER) model?
A. Evaluating sentiment in product reviews, emails, and social media posts.
B. Organizing and tagging content found in resumes, websites, and emails.
C. Connecting user queries to FAQs to deliver accurate responses.
D. Detecting and classifying specific entities in documents like emails, web pages, transcripts, and research papers.
Correct Answer: D
Explanation:
The primary function of a pre-built Custom Named Entity Recognition (NER) model is to identify and classify specific entities in unstructured or semi-structured textual content. These entities often include names of people, organizations, locations, dates, product codes, and other domain-specific terms that are valuable for downstream analysis or automation.
NER models are trained using machine learning techniques, and when "custom" or "pre-built," they are typically optimized for specific domains such as finance, healthcare, legal, or retail. Their purpose is to enable automatic entity detection in a variety of document types including emails, web pages, customer support transcripts, medical records, and research papers.
Let’s examine why the correct answer is D and why the others are incorrect:
A. Evaluating sentiment in product reviews, emails, and social media posts pertains to Sentiment Analysis, not NER. While both tasks fall under the umbrella of Natural Language Processing (NLP), sentiment analysis focuses on determining emotions or opinions expressed in text—such as whether a customer review is positive or negative—rather than extracting entities.
B. Organizing and tagging content found in resumes, websites, and emails describes content classification or document tagging, which might leverage NER in some workflows but is not the core purpose of NER. NER does not organize or classify entire documents; instead, it works at the token level to identify specific elements within the text.
C. Connecting user queries to FAQs to deliver accurate responses refers to intent recognition and semantic search, which are common in chatbots and virtual assistants. This process may use entity recognition in a supporting role but relies more on question answering models, vector search, and natural language understanding (NLU) rather than NER alone.
D. Detecting and classifying specific entities in documents like emails, web pages, transcripts, and research papers is precisely the function of a Custom NER model. This choice highlights both the type of input (diverse document types) and the task (detection and classification of named entities), which aligns directly with what NER models are built to do.
In summary, a Custom NER model is specialized for extracting meaningful, labeled tokens (e.g., "Apple Inc." as an organization, "May 12, 2025" as a date) from diverse documents. These models are foundational in information extraction pipelines, helping businesses and researchers extract structured data from large volumes of text.
Question 2
Which method is most effective for extracting data that varies in length or spans across multiple document pages?
A. Hybrid extraction approach
B. Rule-based extraction
C. Model-only extraction
D. Manual data processing
Correct Answer: A
Explanation:
When dealing with complex documents—especially those with data that spans multiple pages or has variable length fields (like contract clauses, legal provisions, financial tables, or patient histories)—the most effective strategy is a hybrid extraction approach. This method combines machine learning models, often powered by Natural Language Processing (NLP), with rule-based techniques, creating a robust and flexible extraction pipeline.
The hybrid extraction approach balances the generalization capability of models with the precision of rules. For example, machine learning can detect the context and location of an entity, even when the entity doesn’t follow a consistent pattern. On the other hand, rules (like regex, anchors, or spatial layout logic) can enforce specific formatting conditions or structural constraints that models might overlook.
Let’s evaluate each option:
A. Hybrid extraction approach is the correct answer because it effectively handles:
Unpredictable content length (e.g., a "Terms and Conditions" section that might be two lines in one document and two pages in another).
Cross-page elements, such as multi-page tables or invoice details.
Inconsistent formats, like varying invoice layouts from different vendors.
This adaptability comes from using ML models to interpret the semantic context and rules to handle consistent structural patterns or known edge cases.
B. Rule-based extraction alone struggles with variability. It is brittle in cases where documents differ in layout, content phrasing, or formatting. Rules are excellent when documents follow a uniform structure, but they fail when content varies—even slightly.
C. Model-only extraction, while better than rule-based for unstructured data, can suffer from lower precision and limited explainability, especially when handling long spans or documents with inconsistent layout. Without rules, models might misclassify or miss multi-line entities or multi-page spans.
D. Manual data processing is time-consuming, expensive, and prone to human error. It's a fallback when automation fails, but it’s not scalable and certainly not effective for large-scale document processing.
In conclusion, a hybrid extraction approach leverages the strengths of both rules and machine learning, making it uniquely capable of handling complex, variable-length, and cross-page data extractions with high accuracy and scalability.
Question 3
What are the key steps required to allow a remote employee to verify extracted document data using a validation task?
A. Present Validation Station, Pause for Validation, then Continue Workflow
B. Use Orchestration Process Activities
C. Apply Document Understanding Process Activities
D. Create a Validation Task, Await Completion, then Resume Process
Correct Answer: D
Explanation:
In a document automation workflow using platforms like UiPath Document Understanding, there are situations where human validation is required—particularly when the data extracted by AI models is low-confidence, ambiguous, or critical in nature (such as invoice totals or contract terms). In these scenarios, a remote employee or human-in-the-loop validator is introduced into the automation pipeline through a Validation Task.
The correct sequence of steps to enable this human validation is:
Create a Validation Task – This is done using activities such as "Create Validation Action", which generates a task for human users in Action Center. This task contains the extracted data and the original document for review.
Await Completion – After the task is created, the process must pause and wait for the human validator to complete the review. This is typically done using the "Wait for Document Validation Action and Resume" activity. The automation will be suspended until the remote employee confirms or modifies the data.
Resume Process – Once the validation task is completed, the process resumes automatically, continuing with the next steps, such as data entry into a system or archiving the document.
This process ensures that extracted data is accurate and meets business or compliance standards before downstream use.
Let’s break down why the correct answer is D and not the others:
A. Present Validation Station, Pause for Validation, then Continue Workflow is misleading. While Validation Station is used for manual validation, it is not designed for remote use via Action Center. It's more suitable for attended workflows or for use during development/testing by the process designer.
B. Use Orchestration Process Activities refers broadly to a class of UiPath processes designed for long-running workflows. While orchestration processes are involved in enabling human-in-the-loop scenarios, just mentioning them does not specify the steps needed to validate data, which include creating and managing the task.
C. Apply Document Understanding Process Activities is also too general. These activities include digitization, classification, and extraction, but not the orchestration of human validation through tasks. Validation requires specific action management components not covered by this option.
D. Create a Validation Task, Await Completion, then Resume Process is the most precise and accurate sequence. It directly addresses the creation, suspension, and resumption steps that enable remote human validation via UiPath Action Center or similar platforms.
In conclusion, for a remote employee to verify extracted document data as part of a document understanding workflow, the automation must follow a structured pattern: initiate a validation task, wait for its completion, and then continue the process. This ensures high-quality, validated outputs without disrupting the end-to-end automation pipeline.
Question 4
What is the main role of the Taxonomy Manager in a document automation workflow?
A. Assigning extractors to specific document types and fields
B. Building and updating the taxonomy file for the project
C. Selecting the appropriate machine learning model type
D. Providing a user interface for reviewing document classifications
Correct Answer: B
Explanation:
In a document automation workflow, particularly when using platforms like UiPath Document Understanding, the Taxonomy Manager plays a foundational role. Its core responsibility is to build and maintain the taxonomy file, which acts as a structured schema that defines the document types and the fields (also known as data points) to be extracted from each document.
Here’s how it works:
The Taxonomy Manager allows users to define document categories, such as "Invoices," "Purchase Orders," "Receipts," etc.
Within each category, users can specify field definitions, like "Invoice Number," "Vendor Name," "Total Amount," or "Date."
These definitions inform both the classification and extraction stages of the pipeline by giving the models or rule-based extractors a clear map of what to look for in each document type.
This structure is crucial for:
Enabling extractors (whether ML-based or rule-based) to know which fields to target
Ensuring consistency across multiple documents and projects
Supporting downstream processes like validation, exporting, and data mapping
Why B is correct and the others are not:
A. Assigning extractors to specific document types and fields is handled through Extraction activities in the document understanding framework, such as configuring the Data Extraction Scope. This step follows taxonomy creation and is not the role of the Taxonomy Manager itself.
C. Selecting the appropriate machine learning model type is done during the model selection phase—either choosing from pre-trained ML extractors (like Invoices or Receipts) or uploading your own. The Taxonomy Manager does not determine which ML model is used.
D. Providing a user interface for reviewing document classifications is a function of tools like the Validation Station or Action Center, not the Taxonomy Manager. While the taxonomy supports classification logic, it is not the interface for reviewing classifications.
In conclusion, the Taxonomy Manager’s main role is to act as the blueprint for document processing. It establishes a standardized and structured definition of document types and fields, which is essential for enabling consistent extraction, validation, and export operations in a document understanding pipeline.
Question 5
What does "Document Understanding" primarily refer to in automation?
A. Integrating extraction tools across workflow processes
B. A complete system for digitizing, extracting, validating, and training on document data
C. Extracting tables specifically from Excel files
D. Using various methods to identify text in Word documents like contracts
Correct Answer: B
Explanation:
Document Understanding in the context of automation platforms such as UiPath refers to an end-to-end solution that enables organizations to intelligently process unstructured or semi-structured documents by combining OCR, machine learning, and human-in-the-loop validation.
The purpose of Document Understanding is to automate manual document-processing tasks that typically require human effort, such as reading invoices, extracting totals from receipts, classifying contracts, or digitizing handwritten forms. This system follows a full pipeline that includes several stages:
Digitization: Converts scanned or image-based documents into machine-readable text using Optical Character Recognition (OCR).
Classification: Identifies the type of document, such as Invoice, Receipt, Tax Form, etc., using either rule-based or ML-based classifiers.
Data Extraction: Extracts specific fields such as "Invoice Number", "Date", or "Total Amount" using a variety of extractors including machine learning models, regex-based, or form extractors.
Validation: Allows a human validator to verify or correct low-confidence extracted data using the Validation Station or Action Center.
Training: Continuously improves accuracy by retraining custom ML models with new annotated data over time.
Therefore, B is the most comprehensive and correct choice because it captures the entire lifecycle of how automation systems manage document data—from ingestion to validation to feedback-based learning.
Why the other options are incorrect:
A. Integrating extraction tools across workflow processes is a vague and partial description. While integration is involved, it doesn’t encompass the broader digitization, classification, and validation steps that define Document Understanding.
C. Extracting tables specifically from Excel files is not relevant to Document Understanding, since Excel files are already structured and don't require OCR or classification. Document Understanding is typically used on unstructured files like PDFs, scanned documents, and images.
D. Using various methods to identify text in Word documents like contracts is also too narrow. While Document Understanding can work on Word files and contracts, it focuses on the full document lifecycle, not just "text identification."
In conclusion, Document Understanding is a holistic framework for intelligently automating document processing, especially in workflows involving complex, unstructured content such as scanned forms, handwritten applications, or PDFs. It combines multiple technologies to ensure data is accurately extracted, validated, and usable in downstream automation.
Question 6
Which of the following tasks results in the consumption of Page Units?
A. Running OCR on a 10-page document
B. Initiating a Document Validation Action in the Action Center
C. Applying an ML Classifier to a 21-page file
D. Using Intelligent Form Extractor on 5 pages with no successful data found
Correct Answer: A
Explanation:
Page Units are the measurement Snowflake and other intelligent document processing platforms use to track billing and usage for document processing services. In platforms like UiPath Document Understanding, Page Units are consumed when document digitization or extraction activities are performed on a page, especially when OCR or ML-based extractors are invoked.
Let’s analyze the options to determine when Page Units are actually consumed:
A. Running OCR on a 10-page document is the correct answer because OCR (Optical Character Recognition) is a digitization process, and digitization always incurs Page Unit consumption. Whether you're using Google OCR, Microsoft OCR, or UiPath's own OCR engines, every page that undergoes OCR contributes to Page Unit usage. In this case, all 10 pages will definitely consume 10 Page Units (or more, depending on the pricing model).
B. Initiating a Document Validation Action in the Action Center does not consume Page Units. This step involves a human-in-the-loop reviewing previously extracted data. By this stage, the pages have already been digitized and processed. The validation interface may display the data and scanned image, but since no new OCR or extraction is occurring, no additional Page Units are used.
C. Applying an ML Classifier to a 21-page file might seem like it consumes Page Units, but classification activities generally only process the first page unless explicitly configured otherwise. Moreover, most ML Classifier usage is lightweight in Page Unit consumption and may not charge per page in the same way as digitization or extraction. Unless classification includes OCR or intensive processing, this step does not always result in proportional Page Unit use.
D. Using Intelligent Form Extractor on 5 pages with no successful data found is a tricky case. Page Units are consumed regardless of whether data is successfully extracted. However, Intelligent Form Extractor typically requires templates and works best on structured forms. Still, if the engine attempted extraction—even without success—it might count as usage. But this depends on how the engine processed the page. That said, the question implies "no successful data found" as the key detail, suggesting the engine did not meaningfully process the page, making this option less definitive than A.
Therefore, A is the best and most unambiguous choice, as OCRing a document directly incurs Page Unit consumption and represents a clear usage cost within document processing systems.
In conclusion, the act of digitizing documents using OCR is a primary trigger for Page Unit billing, making it essential for architects and developers to understand how and when their automated document workflows are incurring usage charges.
Question 7
Which element must be provided when importing a taxonomy into UiPath Communications Mining?
A. Label prediction data
B. Entity explanations
C. Entity prediction results
D. Descriptions of labels
Correct Answer: D
Explanation:
When importing a taxonomy into UiPath Communications Mining, you are essentially bringing in a structured definition that outlines the different labels and entities that the system will use to categorize and classify communication data (such as emails, transcripts, and chats). A taxonomy in this context is a critical part of the data labeling process used for training and inference. The key aspect of importing a taxonomy is to define how entities are categorized and to associate meaningful explanations with those categories.
Descriptions of labels (Option D) are a crucial element because they give context to the labels that are defined in the taxonomy. These descriptions provide clarity about the meaning or use case for each label, making it easier for the system to perform accurate classifications. Essentially, these descriptions ensure that the system can interpret the labels correctly and apply them in real-world scenarios.
Why the other options are incorrect:
A. Label prediction data refers to the predicted labels that are generated by an ML model after it has processed some data. While label prediction data is important for model performance, it is not a required element when importing a taxonomy itself.
B. Entity explanations are useful for giving further context to specific entities within the taxonomy, but the primary requirement during taxonomy import is typically to provide the descriptions for the labels themselves (which are applied to data). While entity explanations can help clarify the meaning of entities, they are not as central as descriptions of labels.
C. Entity prediction results refers to results from a model that have already been processed, which is not a requirement for importing the taxonomy. Taxonomy import is primarily about establishing the structure of the labels and entities, not about prediction outcomes.
In conclusion, descriptions of labels (Option D) are the essential elements that must be provided when importing a taxonomy into UiPath Communications Mining. These descriptions help the system understand and apply the correct labels to incoming communication data for processing.
Question 8
In which scenario is the ML Classifier best suited according to recommended practices?
A. When document types are very similar and splitting isn't needed
B. When document types are different but no file splitting is necessary
C. When document types are different and file splitting is required
D. When document types are very similar and file splitting is required
Correct Answer: C
Explanation:
The ML Classifier is most effective in scenarios where the document types to be processed are different in nature, and file splitting is required. Here’s why:
When document types vary significantly, such as invoices, receipts, contracts, and forms, it becomes necessary to use an ML Classifier to identify the correct type of document. The ML Classifier uses machine learning algorithms trained on labeled examples to classify documents based on their content and structure. In cases where the document types are not easily distinguished by simple rules or patterns, the classifier excels because it can learn from a large set of training data and generalize to new unseen documents.
The mention of file splitting is important because large documents may contain multiple document types. For example, a single document might include both an invoice and a purchase order, or it could be a contract with an appendix. In these cases, file splitting refers to dividing the document into smaller segments for easier classification. For example, the classifier may first identify the section of the document that corresponds to an invoice, and then separate out the contract part. This makes the ML Classifier particularly useful in scenarios where there is a mix of document types, and splitting helps process them independently.
Why the other options are incorrect:
A. When document types are very similar and splitting isn't needed: If the document types are very similar, then a machine learning classifier may not be the best approach. In these cases, simpler methods like template-based classification or rule-based extraction might be more effective because the differences between document types are minimal, and file splitting isn't necessary.
B. When document types are different but no file splitting is necessary: This scenario may still require an ML Classifier, but the need for file splitting typically indicates that the documents are large or contain multiple sections, making splitting necessary for more accurate processing. Without splitting, the classifier might struggle with accurately identifying sections within a single large document.
D. When document types are very similar and file splitting is required: In cases where the document types are very similar but file splitting is required, simpler methods such as rule-based classification or template matching might be more efficient, especially if the types only differ in small details. An ML Classifier is better suited for more diverse document types, and file splitting may only add unnecessary complexity if the document types don’t differ enough to justify machine learning.
In conclusion, the ML Classifier is best suited for situations where document types are varied and file splitting is necessary to properly handle complex, large, or mixed documents. This ensures that the system can classify and process each part of the document efficiently using machine learning capabilities.
Question 9
What is the default visibility setting of a newly created ML skill?
A. Public by default, can be changed to private
B. Private by default, can be changed to public
C. Public by default, with no option to make it private
D. Private by default, and cannot be made public
Correct Answer: B
Explanation:
When you create a machine learning (ML) skill in systems such as UiPath AI Center, the default visibility setting is private by default. This means that the ML skill is not visible or accessible by others initially, and it is typically only available to the user who created it or to specific users/groups defined by the system's access control mechanisms.
The private setting ensures that sensitive data, processes, or models are protected from being accessed by unauthorized users or the broader user base, especially during the early stages of development or testing. This privacy default also helps prevent the inadvertent sharing of incomplete or experimental ML models until the creator is confident in their functionality and readiness for broader use.
Once the ML skill is fully developed and tested, it can be changed to public if the creator wants to make it accessible to others. The platform allows you to alter this setting to suit the needs of your organization or specific project. For instance, making it public would allow other users or teams within the organization to access, use, or even contribute to the skill.
Why the other options are incorrect:
A. Public by default, can be changed to private: This is incorrect because the default visibility setting is private, not public. While the skill can be changed to private at any point after creation, it starts as private.
C. Public by default, with no option to make it private: This is incorrect because it is possible to change the visibility of a skill from private to public or vice versa. There is always an option to make it private, depending on the system's role-based access controls and permissions.
D. Private by default, and cannot be made public: This is incorrect because, as previously explained, the skill can indeed be made public. The default setting is private, but it is fully possible to change it to public if needed.
In summary, the default visibility setting of a newly created ML skill is private to ensure security and control over access. This can be adjusted to the public later, once the skill is ready for broader usage or sharing across teams or the organization.
Question 10
Which two architectural measures are required to achieve zero-data-loss business continuity for UiPath Orchestrator when it is deployed on-premises? (Choose 2.)
A. Configure the Orchestrator web nodes behind a load balancer in active-active mode
B. Place the SQL Server (Orchestrator database) in an Always On Availability Group with synchronous commit
C. Enable auto-scale rules in IIS to spawn additional worker processes during peak load
D. Back up the Orchestrator web.config file to a network file share every 24 hours
E. Store the Orchestrator logs in an Elasticsearch cluster with cross-site replication
Correct Answer: A, B
Explanation:
To achieve zero-data-loss business continuity for UiPath Orchestrator deployed on-premises, two primary architectural measures are essential:
A. Configure the Orchestrator web nodes behind a load balancer in active-active mode:
This approach ensures high availability for the Orchestrator web nodes. In an active-active load-balanced configuration, multiple web nodes handle traffic simultaneously, improving resilience by ensuring that if one node fails, the others can continue processing requests without interruption. This measure is crucial in achieving zero-data-loss business continuity because it guarantees that Orchestrator remains operational even if one web node fails, maintaining seamless service without impacting data integrity.B. Place the SQL Server (Orchestrator database) in an Always On Availability Group with synchronous commit:
The SQL Server hosting the Orchestrator database must be highly available to ensure zero-data-loss. By configuring the SQL Server in an Always On Availability Group with synchronous commit, real-time data replication between the primary and secondary database servers is maintained. In this configuration, if the primary database goes down, the secondary database can immediately take over without any data loss, thus ensuring the continuity and integrity of the data.
These two measures focus on achieving high availability and data consistency across critical components of UiPath Orchestrator, ensuring that the system can recover quickly and without losing any data, even in case of failures.
Why the other options are incorrect:
C. Enable auto-scale rules in IIS to spawn additional worker processes during peak load:
While auto-scaling rules help with managing load during peak usage periods, they are more about scalability and performance optimization rather than ensuring zero-data-loss business continuity. This is not a sufficient measure on its own to achieve the desired business continuity.D. Back up the Orchestrator web.config file to a network file share every 24 hours:
While backing up the web.config file ensures that you can restore the configuration if needed, it does not address the critical requirement of zero-data-loss continuity for the Orchestrator's operational data. Business continuity for Orchestrator requires more than just backing up configuration files; it demands database availability and real-time data replication.E. Store the Orchestrator logs in an Elasticsearch cluster with cross-site replication:
Storing logs in an Elasticsearch cluster with cross-site replication is useful for ensuring log availability and performance monitoring, but it does not directly contribute to zero-data-loss business continuity for the Orchestrator service. Logs are important for troubleshooting and analysis but do not impact the operational continuity or database consistency required for business continuity.
In conclusion, to achieve zero-data-loss business continuity for UiPath Orchestrator on-premises, you must ensure high availability of both the Orchestrator web nodes and the SQL Server database by using load balancing and synchronous data replication techniques. These steps will ensure that both application and data layers are resilient to failures.
