UiPath-SAIAv1 Exam Dumps - UiPath Specialized AI Associate Questions and Answers

According to the UiPath documentation portal1, the RPA Developer is the role that consumes ML Skills within customized workflows in Studio using the ML Skill activity from the UiPath.MLServices.Activities package. The RPA Developer is responsible for designing, developing, testing, and deploying automation workflows using UiPath Studio and other UiPath products. The RPA Developer can use the ML Skill activity to retrieve and call all ML Skills available on the AI Center service and request them within the automation workflows. The ML Skill activity allows the RPA Developer to pass data to the input of the skill, test the skill, and receive the output of the skill as JSON response, status code, and headers2. Therefore, option C is the correct answer, as it describes the role and the activity that are related to consuming ML Skills in Studio. Option A is incorrect, as the Data Scientist is the role that creates and trains ML models using AI Center or other tools, and publishes them as ML Packages or OS Packages1. Option B is incorrect, as the Administrator is the role that manages the AI Center service, such as configuring the infrastructure, setting up the permissions, and monitoring the usage and performance1. Option D is incorrect, as the Process Controller is the role that deploys ML Packages or OS Packages as ML Skills, and manages the versions, the endpoints, and the API keys of the skills1.

When setting up a classification label in UiPath's Out Of the Box (OOB) labeling templates, you need to provide several key details: the name of the label, the classification type (which defines the kind of label), the input to be labeled, the attribute name that describes the label's context, a shortcut for quick access, and a color for visual distinction. These fields ensure the label is fully defined and easy to manage in workflows.

(Source: UiPath Document Understanding documentation)

According to the UiPath documentation, OmniPaqe is a paid OCR engine that requires a license to use. It is one of the most accurate and reliable OCR engines available, and it supports over 200languages. The other OCR engines listed are free of charge, but they may have different features, limitations, and performance levels. For example, Tesseract is an open-source OCR engine that supports over 100 languages, but it may not be as accurate as OmniPaqe. Microsoft Azure OCR and Microsoft OCR are both cloud-based OCR engines that use Microsoft’s technology, but they have different capabilities and pricing models. Microsoft Azure OCR can process both printed and handwritten text, and it uses a pay-as-you-go model based on the number of transactions. Microsoft OCR can only process printed text, and it is included in the UiPath Studio license.

According to the UiPath documentation, the available options for Scoring in Document Manager, that apply only to string content type, are exact match and Levenshtein. Exact match is a scoring strategy that considers a prediction to be correct only if it exactly matches the true value. Levenshtein is a scoring strategy that measures the similarity between two strings by counting the minimum number of edits (insertions, deletions, or substitutions) required to transform one string into another. The lower the Levenshtein distance, the higher the score. These options can be configured in the Advanced tab of the Edit Field window for string fields.

In this scenario, the rules defined in the Taxonomy Manager are as follows:

IsNotEmpty: Ensures that the field is not empty.

EndsWithUSA: Checks if the extracted address ends with "USA".

ContainsNY: Ensures that the extracted address contains "NY".

The address extracted in this case is: "42 W 80th St. West New York, NJ 1234, USA". While the address ends with "USA" (passing the EndsWithUSA rule), it includes "West New York, NJ", which satisfies the ContainsNY rule even though "NY" is part of "New York". However, the exact behavior of the Contains rule can generate a warning message because "NY" is part of a largerstring ("New York"). Since this does not constitute an error but simply a rule conflict, the validation can proceed.

Therefore, the most accurate outcome would be a warning, but validation can still be performed

 The exported data from the UiPath Document Understanding Template contains the extraction results in a JSON format, along with some additional information that can be useful for debugging or analysis purposes. One of the additional information that can be included is the page number from which the field was extracted and the exact position on the page, represented by the coordinates of the bounding box. This information can help to locate the field on the original document image and to verify the accuracy of the extraction. The additional information can be enabled or disabled by setting the IncludeMetadata parameter to true or false in the Config file of the template.

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.