UiPath-SAIAv1 Questions and Answers

According to UiPath documentation, model versions can be pinned and managed on the "models" tab, ensuring that users can maintain and revert to specific versions when necessary for continuity and performance

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.

As a best practice, Subject Matter Experts (SMEs) should perform the data labeling in UiPath Communications Mining or Document Understanding projects. SMEs have the in-depth knowledge of the specific content and context, which ensures that the data is labeled correctly andmeaningfully for training machine learning models. Their expertise is essential for accurate taxonomy and data preparation

When uploading an ML package in UiPath AI Center with the Enable Training option inactive, several validation checks are performed:

There must be a non-empty root folder.

A requirements.txt file must be present to define dependencies.

A main.py file should be in the root folder, which implements a class Main.

The class must implement the necessary methods, such as __init__ and predict.

For more details, refer to:

UiPath AI Center Documentation: ML Package Validation

 A label taxonomy is a hierarchical structure of concepts that you want to capture from your communications data, such as emails, chats, or calls. Each label represents a specific concept that serves a business purpose and is aligned to your objectives. A label taxonomy can have multiple levels of hierarchy, where each child label is a subset of its parent label. For example, a parent label could be “Product Feedback” and a child label could be “Product Feature Request” or “Product Bug Report”. A label taxonomy is used to train a machine learning model that can automatically classify your communications data according to the labels you defined1.

One of the best practices for designing a label taxonomy is to ensure that each label is clearly identifiable from the text of the individual verbatim (not thread) to which it will be applied. A verbatim is a single unit of communication, such as an email message, a chat message, or a call transcript segment. A thread is a collection of related verbatims, such as an email conversation, a chat session, or a call recording. When you train your model, you will apply labels to verbatims, not threads, so it is important that each label can be recognized from the verbatim text alone, without relying on the context of the thread. This will help the model to learn the patterns and features of each label and to generalize to new data. It will also help you to maintain consistency and accuracy when labelling your data2.

The Shuffle training mode in the "Explore" phase of UiPath Communications Mining helps to introduce randomness into the training data. This prevents labeling bias by ensuring that the model does not overly focus on certain labels or examples, leading to a more balanced and generalized model. By shuffling the data, the model is exposed to a diverse range of examples, which enhances its ability to make accurate predictions across different labels and datasets.

(Source: UiPath Communications Mining documentation)

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 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 tofacilitate easier troubleshooting of failed automations by providing insights into the moments leading to the failure, which can be reviewed later for debugging

According to the UiPath documentation1, the generic Document Understanding ML Package supports data extraction from any type of structured or semi-structured 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 correct answer is D.

UiPath provides three types of classifier trainers to optimize document classification: Keyword Based Classifier Trainer, Intelligent Keyword Classifier Trainer, and Machine Learning Classifier Trainer. These trainers are used to teach the system how to categorize documents based on keywords, intelligent learning patterns, or machine learning techniques for more complex classifications.(Source: UiPath Classifier Trainer documentation​

If Label X has 80% precision at a given confidence threshold, this means that out of every 100 messages predicted to have Label X, 80 are correctly labelled, and 20 are incorrectly assigned the label. Precision measures the correctness of predictions made, so in this case, 80% of the predictions were correct, while 20% were false positives​

In UiPath Communications Mining, the precision and recall statistics for specific labels are primarily determined by the confidence threshold set for the model. This threshold represents the level of certainty the model needs before it assigns a label to a message. By adjusting this threshold, you can trade off between precision (fewer false positives) and recall (fewer false negatives). For instance, a higher threshold will increase precision but may reduce recall, and vice versa​

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 Document Understanding Cloud APIs, the Generative Classifier is used to classify documents by leveraging a generative AI model. To classify a document as either an "Invoice" or a "Receipt," a POST request must be sent to the /api/framework/projects/{projectId}/classifiers/generative_classifier/classification endpoint. The body of this request should contain prompts specifying the classification types (in this case, "Invoice" and "Receipt") along with their corresponding descriptions. This allows the model to correctly classify incoming documents based on these predefined prompts.

For further reading, refer to:

UiPath Document Understanding API Documentation: Cloud API for Classification

Generative Classifier APIs: UiPath Classification APIs

The main purpose of the Document Understanding Process template in UiPath Studio is to provide users with a ready-to-use document processing framework. It is designed to simplify the development of document understanding automations by offering out-of-the-box features like logging, exception handling, and retry mechanisms. This template allows developers to quickly start working on both simple demos and large-scale document processing projects without needing to build everything from scratch. It is equipped with key components such as classifiers and extractors to streamline document classification and data extraction workflows

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. Properand complete labeling is critical to ensure the model learns accurately from the 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)

A screenshot of a computer AI-generated content may be incorrect.

To automatically retrain and deploy a Document Understanding Machine Learning (ML) Model in AI Center with data from the Document Validation Action, the steps should be followed in this order:

Send Human in the Loop data to the used Dataset.

This step involves sending the data that has been validated and corrected by human reviewers to the dataset. This data will be used for training the ML model.

Use the Export feature from Document Manager using Scheduled Export.

After the data is reviewed and validated, it needs to be exported from the Document Manager. Scheduled Export automates this process, ensuring the dataset in AI Center is regularly updated with new data.

Run Training/Full Pipeline in AI Center using Scheduled Pipeline with Auto-Retraining enabled.

With the updated data in the dataset, the next step is to run the training or the full pipeline. The use of Scheduled Pipeline with Auto-Retraining ensures that the ML model is automatically retrained with the latest data.

Enable the Auto Update feature in the ML Skill.

Finally, enabling the Auto Update feature in the ML Skill ensures that the newly trained model is automatically deployed, making the improved model available for document understanding tasks.

Following these steps in the specified order allows for a streamlined process of continuously improving the ML model based on human-validated data, ensuring better accuracy and efficiency in document understanding tasks over time.

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.

Unattended automation is ideal for tasks that can run independently without human intervention, such as scheduled activities during off-hours. Running reports and emailing stakeholders overnight is a perfect use case for unattended bots, which can execute the process autonomously.

The Form Extractor in UiPath is best suited for documents that have a fixed or non-variable format. This means that documents where the layout remains consistent and predictable, such as standardized forms or invoices, are ideal for this extraction method. The Form Extractor usespredefined templates that map data fields based on their positions, making it highly effective for extracting data from documents with little to no variation in layout. It is not well-suited for documents with significant layout variability, which would require a more flexible extraction approach, such as a machine learning extractor.

(Source: UiPath Document Understanding documentation)

According to the UiPath documentation, data labeling is a process that involves uploading raw data, annotating text data in the labeling tool, and using the labeled data to train ML models1. Data labeling is performed by human labelers, who can be either internal or external to the organization2. However, concurrent labeling is not supported by the UiPath Data Labeling tool, which means that only one user can label a document at a time3. If multiple users try to label the same document concurrently, they will encounter an error message that says “The document is locked by another user. Please try again later.”. Therefore, the correct answer is C.

Comprehensive and Detailed Explanation From Exact Extract:

If a Try Catch activity has no defined Catch blocks and the Try section throws an exception, the exception is not caught and will propagate, potentially terminating the workflow.

However, if no exception occurs, the process continues normally.

The activity does not result in validation errors just because Catch blocks are empty.

UiPath Documentation Reference:

Try Catch Activity – UiPath Docs

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When creating an AI Center project in UiPath, the fields available to input are the project's nameand description. These fields allow you to clearly label and describe the purpose of the AI project within the UiPath platform. Permissions and labels can be managed separately after the project is created​

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.

The DocumentUnderstanding ML package is a generic, retrainable model designed to handle various document types that are not covered by out-of-the-box models. It allows for the extraction of data from structured and semi-structured documents by building a model from scratch through training. This package is highly flexible and can be tailored to fit different document formats, making it ideal when specific document types are not pre-configured in UiPath’s out-of-the-box offerings.(Source: UiPath Documentation on ML Packages

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).

Comprehensive and Detailed Explanation From Exact Extract:

In the context of automation and digital workflows, adigital business processconsists of:

Inputs: The raw data required

Process flows: The sequence of tasks or actions

Outputs: The final results generated

Assignees and source applications aresupporting components, but not universally required in every digital process.

UiPath Academy Reference:Automation Hub Training → Defining Business ProcessesUiPath Process Mining – Fundamentals

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.

Comprehensive and Detailed Explanation From Exact Extract:

The"Step Out"option in UiPath Studio’sDebugging toolbar, as shown in the image, is used when debugging a process and you have steppedinside a function, invoked workflow, or nested container. If you want toexit from the current container (e.g., a workflow or sequence)and return to thecaller or parent scope, you useStep Out.

Itexecutes all remaining activities within the current container, and once complete,pauses the executionback at the point where that container was invoked.

Itdoes not stop execution, nor does it re-execute exceptions or pause after every activity (like Step Into or Step Over).

????Visual Confirmation from the Image:

The "Step Out" button is highlighted in red, indicating it’s active and available during debugging.

It is grouped alongside "Step Into" and "Step Over," all part ofdebug control options.

????Use Case:

Suppose you're debugging a workflow and step into an invoked file or a "Then" branch. If everything looks fine, you can useStep Outto quickly exit and return control to the parent workflow without stepping through every line.

UiPath Documentation Reference:Debugging in Studio – UiPath Docs

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 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.

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​

The Quality of Service (QoS) feature in UiPath Communications Mining is designed to monitor and measure the quality of service within a communications channel. It helps businesses understand how well their communications are being managed by evaluating different performance metrics such as response times and the quality of interactions. QoS ensures that communications are aligned with service level agreements and business expectations.

(Source: UiPath Communications Mining documentation)

For the described requirements:

ApplyOcrOnPdf set to Auto ensures OCR is applied only when needed.

DegreeOfParallelism set to -1 uses all available logical processors for maximum parallelism.

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.

Comprehensive and Detailed Explanation From Exact Extract:

Processes that undergofrequent business logic changesrequire frequent updates to automation scripts, resulting inhigh maintenance effortand potential breakdowns in unattended execution.

Stable, rules-based processes are preferred for long-term, low-maintenance automation.

UiPath Academy Reference:RPA Developer Foundation – “Process Assessment and Selection” Module

In UiPath AI Center, versioning of ML packages is an integral part of managing the lifecycle of machine learning models. Each ML package has a major and minor version to track changes. When an ML package is initially uploaded, it is assigned a major version (e.g., 1.0). If a package is retrained via pipelines, it increments the minor version (e.g., from 1.0 to 1.1). This mechanism helps differentiate between user-uploaded packages and those that have been retrained. Only packages that undergo retraining through pipelines modify their minor version, not the major version. Thus, answer D is correct.

(Source: docs.uipath.com on ML Package Management).

Comprehensive and Detailed Explanation From Exact Extract:

The recommended practice in Communications Mining is to train both labels and general fields simultaneously. This ensures the system learns the relationships and intent expressions effectively.

This approach helps improve the overall model accuracy by leveraging training signals from both classification and field extraction.

UiPath Documentation Reference: UiPath Communications Mining Documentation – Labeling Strategy

In UiPath Communications Mining, importing a taxonomy should be done before starting model training. The taxonomy, which includes labels and categories, defines how the data will be classified and structured during the training process. It is essential to have a well-defined taxonomy to ensure accurate predictions and classifications. Importing the taxonomy before training allows the model to learn from it, enhancing its performance. Changes to the taxonomy can be made later, but the initial import is crucial at the start of the training phase to guide the model effectively.

(Source: UiPath Docs on Communications Mining)

The "Wait for Classification Validation Task and Resume" activity in UiPath's Document Understanding Framework is primarily used to halt or suspend the workflow until a specified document classification validation task is completed by a human. This activity is part of the broader workflow to ensure that when automatic classification of documents cannot be confidently achieved, a human-in-the-loop (HITL) approach is followed to validate or correct classifications. Once the validation is performed in UiPath's Action Center by a human, the workflow is resumed, ensuring the proper handling of documents that require review and correction.

This is aligned with the design of the Action Center, which is integrated into UiPath's Document Understanding Framework. When dealing with document classification or extraction confidence issues, manual human validation tasks are often required, which is what this activity manages. It facilitates human oversight, preventing the automation from proceeding with potentially incorrect classifications.

Reference from UiPath documentation:

UiPath Action Center explains how humans are involved in validation tasks to handle cases where classification or extraction needs manual review.

Wait for Task and Resume Activity in UiPath Documentation explains how it waits for a task (such as document validation) to be completed in the Action Center before resuming the workflow.

For more details, you can consult the official UiPath documents:

UiPath Document Understanding Framework

Wait for Classification Validation Task and Resume

This functionality ensures that incorrect data processing due to automation can be caught and rectified by a human, improving accuracy in document handling workflows.

Comprehensive and Detailed Explanation From Exact Extract:

Themain purpose of Document Understandingis to help developers extract structured information fromunstructured documents(invoices, receipts, forms, etc.) using AI and OCR.

It streamlines the entire pipeline ofdigitizing, classifying, extracting, and validatingdocument data.

UiPath Documentation Reference:Document Understanding Overview

Making an ML Skill public in UiPath enables it to be accessed externally from the UiPath ecosystem. This can be beneficial if the ML Skill needs to be utilized in external applications, systems, or services beyond UiPath's automation environment. Public access expands the usability of the skill, allowing integration with other systems while maintaining security through managed endpoints.

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.

Comprehensive and Detailed Explanation From Exact Extract:

UiPath Orchestrator assets supportper-user values, allowing different users (robots) to retrievecustomized valuesfor the same asset key. This is commonly used for credentials, URLs, or configurations that vary per user or environment.

While most asset types (text, integer, credential) support secure handling,only Credential assets are encryptedby default.

UiPath Documentation Reference:Assets in Orchestrator

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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 UiPath Communication Mining, the Generative Annotation feature automatically enables Assisted Labelling on datasets. Assisted Labelling helps to accelerate the labeling process by automatically suggesting relevant labels based on the content of the communications. This feature significantly improves the efficiency of the model training process by reducing the manual effort required to label large datasets.

For more details, refer to:

UiPath Communication Mining Documentation: Generative Annotation and Assisted Labelling

Labeling and Annotation in UiPath Communications Mining: UiPath AI Center Documentation

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

When using theGenerative Extractorin Document Understanding, the best way to increase accuracy and reliability is byspecifying a clear output format. This helps the model understand what kind of response is expected and improves parsing consistency.

Example: "What is the invoice number? Please return in the format: InvoiceNumber: "

UiPath Documentation Reference:Using Generative Extractor – Document Understanding

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A screenshot of a computer AI-generated content may be incorrect.

Export the package from AI Center. This is the first step where you prepare the package to be moved.

On the ML Packages page, click the Import ML Package button. This step is where you start the process of importing the package you've exported.

On the Upload package field, add the zip file downloaded using the Downloading ML Packages procedure. After starting the import process, you will upload the actual package.

Click Create. This is the final step where you finalize the uploading process of your ML package.

Please proceed with these steps in the UiPath AI Center to upload your exported package correctly.