UiPath-SAIv1 UiPath Certified Professional Specialized AI Professional v1.0 Questions and Answers

The Solution Architect is responsible for devising a strategy to prioritize processes during the Business Case and Technical Validation phase. Their role involves assessing technical feasibility, scalability, and business value to determine process prioritization.

In the context of a machine learning pipeline, specifically within the train.py file in UiPath AI models, the function responsible for persisting the trained model is typically named save(self). This function is invoked after the model has been trained, and it ensures that the model's state, weights, and configurations are saved to disk or another persistent storage medium. The saved model can then be reused for inference or further fine-tuning in future executions.

For more details, refer to:

UiPath AI Center Documentation: Training Models

Model Persistence: Saving Machine Learning Models in AI Center

To configure the Classification Station as an attended activity in UiPath, certain inputs are mandatory for proper functionality. These include:

Taxonomy: The schema that defines the structure of document types and fields.

Document Path: The file path of the document to be classified.

Document Object Model (DOM): Generated from the document using the Digitize Document activity, this is a structured representation of the document.

Document Text: The extracted text of the document, also an output from the Digitize Document activity.

These inputs allow the Classification Station to review and validate the classification results, either manually or based on automatic suggestions from previous processes.

In UiPath Communications Mining, the Label Summary report tab provides a high-level overview and statistics on all labels. This summary gives insights into the performance and distribution of labels across the dataset, helping users understand how well the model is classifying different types of communications.

For more details, refer to:

UiPath Communications Mining Documentation: Label Summary Reports

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.

In UiPath Document Understanding, the Digitized Object Model (DOM) is an XML structure that contains detailed information about the document being processed. This includes data such as the content of each page, the coordinates of each element within the document, and additional metadata. The DOM is created during the digitization process, where scanned or image-based documents are converted into machine-readable formats for further processing, such as classification and data extraction.

For more details, refer to:

UiPath Document Understanding Documentation: Digitization and the DOM

Digitize Document Activity: Working with DOM

In the UiPath 2023.10 release, a key improvement introduced was the ability to record and store failed queue transactions for debugging purposes. This feature helps users by capturing the failure details and making them accessible directly in Orchestrator. The recording of failed transactions is enabled at the process level, within the Video section of the Job recording settings. It is intended to facilitate easier troubleshooting of failed automations by providing insights into the moments leading to the failure, which can be reviewed later for debugging

The RegEx Based Extractor is most effective for documents where data fields follow a strict and predictable format, such as dates, invoice numbers, or specific patterns like email addresses. This method is well-suited for extracting information from structured or semi-structured documents with consistent formats across different documents, making it highly reliable for use cases where patterns are easily identifiable and do not vary significantly.

(Source: UiPath Document Understanding documentation)

The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:

Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.

Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.

Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.

Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported.

Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.

References: AI Center - Managing Data Labels, Data Labeling for Text - Public Preview

Recall is a metric that measures the proportion of all possible true positives that the model was able to identify for a given concept1. A true positive is a case where the model correctly predicts the presence of a concept in the data. Recall is calculated as the ratio of true positives to the sum of true positives and false negatives, where a false negative is a case where the model fails to predict the presence of a concept in the data. Recall can be interpreted as the sensitivity or completeness of the model for a given concept2. For example, if there are 100 verbatims that should have been labelled as ‘Request for information’, and the model detects 80 of them, then the recall for this concept is 80% (80 / (80 + 20)). A high recall means that the model is good at finding all the relevant cases for a concept, while a low recall means that the model misses many of them.

References: 1: Recall 2: Precision and Recall

When you create a training dataset for document classification or data extraction, you need to split your documents into two subsets: one for training the model and one for evaluating the model. The training subset is used to teach the model how to recognize the patterns and features of your document types and fields. The evaluation subset is used to measure the performance and accuracy of the model on unseen data. The evaluation subset should not be used for training, as this would bias the model and overfit it to the data1.

The recommended split of documents for training and evaluation depends on the size and diversity of your data. However, a general guideline is to use a 70/30 or 80/20 ratio, where 70% or 80% of the documents are used for training and 30% or 20% are used for evaluation. This ensures that the model has enough data to learn from and enough data to test on. For example, if you have 15 documents per vendor, you can use 10 documents for training and 5 documents for evaluation. This would give you a 67/33 split, which is close to the 70/30 ratio. You can also use the Data Manager tool to create and manage your training and evaluation datasets2.

References: 1: Document Understanding - Training High Performing Models 2: Data Manager - Creating a Dataset

From the provided workflow:

The Assign Status activity sets the variable Status to the value "Approved".

The Check Status activity evaluates the value of Status against defined conditions.

It has branches for "Pending", "New", and "Unknown".

The "Default" branch is triggered if none of these conditions match the value of Status.

Since "Approved" does not match "Pending", "New", or "Unknown", the Default branch will execute.

The Write Line activity in the Default branch outputs "Status not valid." to the Output panel.

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

In UiPath Communications Mining, the shading of label predictions in the Explore section indicates confidence levels. Darker shading represents a higher confidence score, meaning the model is more certain of the prediction made for that particular label​

 According to the UiPath documentation, UiPath Communications Mining is a tool that enables you to analyze text-based communications data, such as customer feedback, support tickets, or chat transcripts, using natural language processing (NLP) and machine learning (ML) techniques1. One of the features of UiPath Communications Mining is the Comparison page, which allows you to compare two cohorts of verbatims based on different criteria, such as date range, source, metadata, or label2. The Comparison page displays the following information for each cohort3:

Total verbatim count: The number of verbatims in the cohort.

Proportion for each label: The percentage of verbatims in the cohort that are assigned to each label. A label is a category or a topic that is relevant for the analysis, such as sentiment, intent, or issue type. Labels can be predefined or custom-defined by the user.

Statistical significance: The p-value that indicates whether the difference in proportions between the two cohorts is statistically significant or not. A p-value less than 0.05 means that the difference is unlikely to be due to chance.

The Comparison page also provides a visual representation of the proportions for each label using a bar chart, and allows the user to drill down into the verbatim content for each label by clicking on the bars3. Therefore, the correct answer is A.

References:

1: About Communications Mining 2: Communications Mining - Comparing Cohorts 3: Communications Mining - Comparison Page

To configure the Validation Station as an attended activity in UiPath, the minimum required inputs include the Taxonomy, which defines the structure and fields for data extraction, the Document Path, the Document Object Model (DOM), the Document Text obtained during digitization, and the Automatic Extraction Results, which are the results from automatic data extraction activities that need validation. These inputs allow the Validation Station to properly display and validate extracted data​

When designing a taxonomy for document types in UiPath, a key consideration is to structure it in a way that maximizes efficiency and reusability. Grouping related document types under the same taxonomy helps to simplify processing and reduce redundancy. This approach ensures that similar document types are treated consistently, making it easier to apply extraction methods and post-processing rules across different but related document types. Over-segmentation into separate taxonomies for each document type can lead to unnecessary complexity and confusion, making management and scaling of automation workflows more difficult. The goal is to create a cohesive structure that can handle various document types effectively.

(Source: UiPath Document Understanding and Communications Mining documentation)

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

Communications Mining - Improving entity performance

After the data is extracted using the UiPath Document Understanding framework, it is crucial to handle the data securely and efficiently for further use in other processes or storage. Serialization is often used to convert the extracted data into a format suitable for storage or transmission. By serializing the data, it can be stored in storage buckets (such as cloud storage or a database) and accessed later as needed for subsequent processing. This approach ensures that data is preserved, secure, and available for other workflows or automations.

(Source: UiPath Document Understanding Documentation).

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 semi-structured 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

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

References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction

It is essential to analyze the languages involved in a project because different components of UiPath's Document Understanding framework have varying language support. For instance, some OCR engines and classifiers support specific languages, and this determines whether a particular language can be processed accurately. As the supported languages differ across components such as the Machine Learning Extractor, Form Extractor, and various OCR services, selecting the right components is crucial to meeting project requirements​

To enable the Taxonomy Manager button in the Ribbon, the UiPath.IntelligentOCR.Activities package must be installed. This package includes all activities related to Document Understanding, including the Taxonomy Manager.

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.

In UiPath Communications Mining, high coverage ensures that a larger proportion of communications is classified with meaningful labels, meaning fewer communications are sent for manual review and more processes are captured for automation. This leads to more effective automation, reducing the need for human intervention and ensuring that the automation use case is fully realized.

For more details, refer to:

UiPath Communications Mining Performance Metrics: Coverage and Automation

In UiPath AI Center, the platform supports several types of machine learning (ML) models, including:

Out-of-the-box models from UiPath: Pre-built models designed for common automation tasks.

Models from UiPath technology partners: External models developed by UiPath’s partners.

Open-source models: Community-contributed models that can be used and adapted for various use cases.

Custom models: Models that users build and train specifically for their projects using their datasets.

This flexibility in model support ensures that organizations can leverage a wide range of machine learning capabilities to suit different automation needs.

For more details, refer to:

UiPath AI Center Documentation: AI Center Models

Machine Learning Model Types: Types of Models in UiPath AI Center

The most likely cause of the error is that the dictionary was not initialized. In UiPath, a dictionary must be initialized before assigning values to its keys. If you attempt to add a key-value pair to a dictionary that has not been initialized, you will encounter a runtime error.

The correct initialization can be done as follows:

PinMapping = New Dictionary(Of String, String)

Explanation of Other Options:

A. PinMapping["John Doe"]: This is the correct syntax for accessing or assigning a value to a dictionary key. There is no issue with this syntax.

B. The "John Doe" key not present: While it is true that the key might not exist, this specific error would occur only during a Get operation, not an assignment.

D. PinMapping<"John Doe">: This is incorrect syntax for working with dictionaries in UiPath.

Partially labeled messages in UiPath Communications Mining can negatively impact model performance because incomplete or inconsistent labeling creates ambiguity for the model during training. This leads to the model receiving conflicting signals, which hampers its ability to generalize well across the dataset, resulting in reduced precision and recall in predictions. Proper and complete labeling is critical to ensure the model learns accurately from the data

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

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

The Custom Named Entity Recognition out-of-the-box model is a machine learning package that allows you to bring your own dataset tagged with entities you want to extract from unstructured text. The model can be trained and deployed using the UiPath AI Center, and can be integrated with the UiPath Document Understanding framework. The model can be used to extract and classify text in various domains and formats, such as emails, letters, web pages, research papers, and call transcripts. For example, you can use the model to extract information such as names, dates, addresses, amounts, products, or any other custom entity from your documents. The model supports multiple languages and can be customized according to your needs.

References: AI Center - Custom Named Entity Recognition, Custom Named Entity Recognition Documentation, UiPath AI Center - Document Understanding & Use of UiPath Custom Named Entity Recognition Model

In UiPath Communications Mining, general fields that are trained from scratch require user-defined inputs and training data, making them highly customizable but dependent on the specific data provided by the user. In contrast, pre-trained general fields are based on predefined rules and training models developed by UiPath. These pre-trained fields offer out-of-the-box functionality and are optimized for common use cases, whereas user-trained fields offer more flexibility to meet specific business requirements.

(Source: UiPath Communications Mining documentation)

Here is the correct order to configure the Extractor Wizard in UiPath Document Understanding:

Step 1: Add a Data Extraction Scope activity to the workflow.Step 2: Place one or more extractors.Step 3: Click on the Configure Extractors button.Step 4: Select the checkboxes next to each field for the extractor type that should be activated.Step 5: Get capabilities (if needed).Step 6: Click on the Save button.

This sequence ensures that the Extractor Wizard is correctly configured to work with the Document Understanding workflow.

Document Understanding documentation, when using the "Export Now" tab in the Export Files dialog box within the Document Manager, the available options include:

Download to Excel – This allows downloading the dataset locally in an Excel format.

Export to AI Center – This exports the data directly to the AI Center for use in model training or evaluation.

Export All – This option exports all documents regardless of labeling or filtering status.

These features are designed to facilitate exporting labeled data for further processing in AI Center, or locally for offline analysis

The synchronous API calls for UiPath Document Understanding Cloud are ideal when you need real-time interaction and fast feedback, such as when processing small documents (with up to five pages). This approach is beneficial in use cases requiring low latency, but it is less suitable for large datasets or documents due to limitations in speed and page count

 Adding missed labels helps improve the label precision and recall in UiPath Communications Mining. Precision is the percentage of correctly labeled verbatims out of all the verbatims that have the label applied, while recall is the percentage of correctly labeled verbatims out of all the verbatims that should have the label applied. By adding missed labels, you are increasing the recall of the label, as you are reducing the number of false negatives (verbatims that should have the label but do not). This also improves the precision of the label, as you are reducing the noise in the data and making the label more informative and consistent. Adding missed labels is one of the recommended actions that the platform suggests to improve the model rating and performance of the labels.

References: Communications Mining - Training using ‘Check label’ and ‘Missed label’, Communications Mining - Model Rating

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

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

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

In UiPath, the Verbose log level offers the most detailed information about the execution of activities. It logs every possible detail about the automation operations, including variable changes, function calls, and external responses. This level is particularly useful for in-depth debugging and analysis.

UiPath Documentation

The hierarchy of log levels in ascending order of priority is as follows:

Off: No logs are stored.

Verbose: Logs all details about automation operations.

Trace: Logs finer-grained informational events than the Debug level.

Information: Logs informational messages that highlight the progress of the application.

Warning: Logs potentially harmful situations.

Error: Logs error events that might still allow the application to continue running.

Fatal: Logs very severe error events that will presumably lead the application to abort.

Therefore, setting the log level to Verbose ensures that all possible details about the execution are captured, aiding in thorough diagnostics.

 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 the ‘Modify datasets’ permission3.

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 type, source, date range, and label category.

Threads: Users can view charts of thread volumes and label volumes within a thread, if the data is in thread form, such as call transcripts or email chains. Users can also filter by data type, source, date range, and label category.

Comparison: Users can compare different cohorts of data against each other, such as different sources, time periods, or label categories. Users can also filter by data type, source, date range, and label category.

References: 1: Communications Mining - Using Reports 2: Communications Mining - Reports 3: Communications Mining - Using Dashboards : [Communications Mining - Using Label Summary] : [Communications Mining - Using Trends] : [Communications Mining - Using Segments] : [Communications Mining - Using Threads] : [Communications Mining - Using Comparison]

 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

When a pipeline is in the running state in UiPath AI Center, the only action that can be performed is to Kill it. This terminates the process immediately, stopping any further processing. Actions like removing or restarting the pipeline can only be performed after the pipeline has stopped.(Source: UiPath Pipeline documentation​