UiPath UiPath Certified Professional Specialized AI Professional v1.0 UiPath-SAIv1 Exam Dumps: Updated Questions & Answers (October 2025)

In UiPath Document Manager, for string fields, the available options for scoring include:

Exact match: This checks whether the extracted string exactly matches the expected value.

Levenshtein: This method computes the "distance" between two strings, which is the number of single-character edits (insertions, deletions, or substitutions) required to change one string into the other. It is used to assess the similarity between the extracted string and the expected value.

These scoring methods help evaluate the accuracy of string extraction in the document understanding process.

For more details, refer to:

UiPath Document Understanding Framework: String Field Scoring

Levenshtein Distance in UiPath: Scoring String Fields

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.

to load a taxonomy from a given non-default location text file into a variable, the order of steps should be as follows:

Create a new variable of type 'DocumentTaxonomy' called 'documentTaxonomy', then create a new string variable called 'taxonomyPath'.

This step involves setting up the necessary variables that will be used in the process. The 'documentTaxonomy' variable will hold the deserialized taxonomy object, and 'taxonomyPath' will store the path to the taxonomy file.

Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.

Here you will assign the path of the taxonomy file to the 'taxonomyPath' variable.

Use the Read Text File activity on the 'taxonomyPath' variable created. Next, create a new variable 'taxonomyText', then save the output of the Read Text File activity to 'taxonomyText'.

This step is where you read the contents of the taxonomy file using the 'Read Text File' activity. The contents are stored in the 'taxonomyText' variable.

Inside the last Assign activity 'Value to Save' property add the following expression: "Newtonsoft.Json.JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText)" where 'taxonomyText' is the text read from the file and 'documentTaxonomy' (the most recent variable created) is the variable you created.

In this final step, you will deserialize the JSON content from the 'taxonomyText' into a 'DocumentTaxonomy' object using the 'JsonConvert.DeserializeObject' method and assign it to the 'documentTaxonomy' variable.

Following these steps in this order will load the taxonomy from a text file into the 'documentTaxonomy' variable in UiPath.

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.

References:

AI Center - Managing Datasets

AI Center - About Datasets

AI Center - About Pipelines

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

Deep learning is a subset of machine learning that uses multiple layers of artificial neural networks to learn from data and perform complex tasks. The term “deep” refers to the number of layers in the network, which can range from a few to hundreds or even thousands. Each layer consists of a set of nodes that perform mathematical operations on the input data and pass the output to the next layer. The network learns by adjusting the weights of the connections between the nodes based on the feedback from the desired output. Deep learning can handle various types of data, such as images, text, speech, or video, and can automatically extract features and patterns from them without human intervention. Deep learning is behind many applications of artificial intelligence, such as computer vision, natural language processing, speech recognition, and generative models123.

References: 1: What is Deep Learning? | IBM 2: What Is Deep Learning? Definition, Examples, and Careers | Coursera 3: Deep learning - Wikipedia

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

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

In the Document Understanding framework, the dispatcher is responsible for ensuring that one job is created for each input file. It works by submitting files to be processed individually, ensuring that each document or group of documents is handled as a separate transaction. This allows for more efficient processing and better tracking of each file, especially in high-volume workflows where managing each file as a separate job is critical for performance and error handling.

(Source: UiPath Documentation on Document Understanding)