UiPath-SAIv1 UiPath Certified Professional Specialized AI Professional v1.0 Free Practice Exam Questions (2025 Updated)

When creating a data labeling session in UiPath AI Center, the key pieces of information required are:

The data labeling session name: A unique identifier for the session.

The dataset: The data that will be used in the labeling session.

For more details, refer to:

UiPath AI Center Documentation: Data Labeling Sessions

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

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)

In UiPath's Document Understanding Framework, UiPath Document OCR is recommended as the initial OCR engine for processing documents. This OCR option is optimized for extracting text from a wide variety of document types and provides good accuracy with structured, semi-structured, and unstructured documents. It is designed to work seamlessly within the UiPath Document Understanding process and is highly compatible with other UiPath activities and models, making it a preferred choice for starting document processing in most projects.

For more details, refer to:

UiPath Document Understanding Framework: UiPath OCR Engines

UiPath Document OCR: UiPath OCR Guide

In UiPath Communications Mining, pinning a model ensures that the predictions shown in the Explore tab are generated from that specific model version. This feature allows users to control which version of the model is actively making predictions, particularly during evaluation or comparison stages. By pinning a model, the user ensures that the UI reflects the predictions from the selected version, helping maintain consistency when analyzing results or making changes.

For more details, refer to:

UiPath Communications Mining: Model Management and Pinning

UiPath AI Center Documentation: Managing Model Versions

According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:

Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as-is for inference or prediction. They do not require any additional data or training from the user. Examples of pre-trained models are Language Detection, Sentiment Analysis, and Question Answering.

Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.

Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine-tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre-trained model and the flexibility of customizing it to the user’s needs. Examples of fine-tunable models are Object Detection, Text Summarization, and Language Translation.

References:

1: Out-of-the-Box Packages

In UiPath Communications Mining, the term Coverage refers to the proportion of messages in the dataset that have informative label predictions. This is an important metric that indicates how much of the dataset the model is able to classify with meaningful and relevant labels. High coverage means that the model is effectively assigning labels to a large portion of the data, which is crucial for ensuring the model’s usefulness in automating communication mining tasks.

For more details, refer to:

UiPath Communications Mining Performance Factors: Model Coverage and Accuracy

Communications Mining: Coverage and Other Metrics

The Data Extraction Scope activity provides a scope for extractor activities, enabling you to configure them according to the document types defined in your taxonomy. The output of the activity is stored in an ExtractionResult variable, containing all automatically extracted data, and can be used as input for the Export Extraction Results activity. This activity also features a Configure Extractors wizard, which lets you specify exactly what fields from the document types defined in the taxonomy you want to extract1.

The extractors that can be used for Data Extraction Scope activity are:

Regex Based Extractor: This extractor enables you to use regular expressions to extract data from text documents. You can define your own expressions or use the predefined ones from the Regex Based Extractor Configuration wizard2.

Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the position and relative distance of the fields. You can define the templates for each document type using the Form Extractor Configuration wizard3.

Intelligent Form Extractor: This extractor enables you to extract data from semi-structured documents, such as invoices, receipts, or purchase orders, based on the labels and values of the fields. You can define the fields for each document type using the Intelligent Form Extractor Configuration wizard.

Machine Learning Extractor: This extractor enables you to extract data from any type of document, using a machine learning model that is trained on your data. You can use the predefined models from UiPath or your own custom models hosted on AI Center or other platforms. You can configure the fields and the model for each document type using the Machine Learning Extractor Configuration wizard.

References: 1: Data Extraction Scope 2: Regex Based Extractor 3: Form Extractor : Intelligent Form Extractor : Machine Learning Extractor

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​

Supervised learning is one of the most popular and widely used machine learning approaches. It involves providing the algorithm with labeled input/output pairs, which serve as examples of the desired behavior or outcome. The algorithm then learns a function that can generalize from these examples and make predictions for new, unseen data. Supervised learning can be used for tasks such as classification, regression, and anomaly detection. Some common supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, and neural networks.

References:

UiPath AI Fabric - Machine Learning Concepts

UiPath Document Understanding - Machine Learning Models

UiPath Communications Mining - Overview

The Document Object Model (DOM) is a data representation of the objects that comprise the structure and content of a document on the web1. In the context of Document Understanding, the DOM is a JSON object that is generated by the Digitize Document activity, which uses the UiPath Document OCR engine to extract the text and layout information from the input document2. The DOM contains the following properties for each document3:

name: The name of the document file.

contentType: The MIME type of the document file, such as application/pdf or image/jpeg.

textLength: The number of characters in the document text.

pages: An array of objects, each representing a page in the document. Each page object has the following properties:

pageNumber: The number of the page, starting from 1.

rotation: The angle of rotation of the page, in degrees. A positive value indicates clockwise rotation, and a negative value indicates counterclockwise rotation.

language: The language code of the page, such as en or fr.

content: An array of objects, each representing a word or a line in the page. Each content object has the following properties:

type: The type of the content, either word or line.

text: The text of the content.

boundingBox: An array of four numbers, representing the coordinates of the top-left and bottom-right corners of the content, in the format [x1, y1, x2, y2]. The coordinates are relative to the page, with the origin at the top-left corner, and the unit is pixel.

confidence: A number between 0 and 1, indicating the confidence level of the OCR engine in recognizing the content.

The DOM can be used as an input for other activities in the Document Understanding framework, such as Classify Document Scope, Data Extraction Scope, or Export Extraction Results. The DOM can also be manipulated using programming code, such as JavaScript or Python, to perform custom operations on the document data.

References:

1: Introduction to the DOM - Web APIs | MDN 2: Digitize Document 3: Document Object Model

his is confirmed in the UiPath documentation, where the "Train" feature guides users from the dataset creation stage through the necessary steps to optimize the training process. The system recommends next best actions based on progress to ensure efficient and focused training​

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

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.

According to the UiPath Communications Mining documentation, coverage is one of the four main factors that contribute to the model rating, which is a holistic measure of the model’s performance and health. Coverage assesses the proportion of the entire dataset that has informative label predictions, meaning that the predicted labels are not generic or irrelevant. Coverage is calculated as the percentage of verbatims (communication units) that have at least one informative label out of the total number of verbatims in the dataset. A high coverage indicates that the model is able to capture the main topics and intents of the communications, while a low coverage suggests that the model is missing important information or producing noisy predictions.

References:

Communications Mining - Understanding and improving model performance

Communications Mining - Model Rating

Communications Mining - It’s All in the Numbers - Assessing Model Performance with Metrics

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)

Create the new label by assigning it at least once. This is the initial step to introduce a new category or classification within your data taxonomy.

Search for instances in the reviewed data where the new label should have been assigned, and apply the label accordingly. This step is crucial for maintaining consistency across your data set.

Use 'Missed Label' to find all additional missing examples in the reviewed data. This action helps in identifying and rectifying any instances that may have been overlooked during the initial review.

Check validation to ensure the label is performing as expected, and follow recommended actions if further training is required. Validation is key to assess the accuracy and performance of the new label within the system.

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