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Questions UiPath UiPath-SAIv1 Exam & UiPath-SAIv1 Actual Dumps

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q114-Q119):

NEW QUESTION # 114
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

A. The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.
Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
B. The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
C. Consider the document types, language, and data quality when choosing an ML Extractor.
It is important to select one that is specifically trained or optimized for the document types being processed.
It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
D. The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.
Opt for the ML Extractor that has the highest number of downloads or positive reviews.

Answer: C

NEW QUESTION # 115
What is the difference between the Document Understanding Process and the Document Understanding Framework?

A. The Document Understanding Framework contains the activities that can be used in a Library, while the Document Understanding Process is the template that can be found in Studio.
B. The Document Understanding Process contains the activities that can be used in a Library, while the Document Understanding Framework is the template that can be found in Studio.
C. The Document Understanding Framework contains the activities that can be used in a Process, while the Document Understanding Process is the template that can be found in Studio.
D. The Document Understanding Process contains the activities that can be used in a Process, while the Document Understanding Framework is the template that can be found in Studio.

Answer: C

NEW QUESTION # 116
What do entity predictions refer to within UiPath Communications Mining?

A. The understanding of the parent-label relationship when assigning label predictions.
B. The identification of a specific span of text as a value for a particular entity type.
C. The model's confidence that a specific concept exists within a communication.
D. The difference between label suggestions and label predictions in a training process.

Answer: B

NEW QUESTION # 117
What is DOM in the context of Document Understanding?

A. Data Object Model is a YAML object containing information such as name, content type, text length, and the number of pages.
B. Digitized Object Model is an XML object containing information such as page data, content, and coordinates for every image identified in the file.
C. Digitize Object Module is an XML object containing information such as mandatory field names, types, and values
D. Document Object Model is a JSON object containing information such as name, content type, text length, and the number of pages.

Answer: B

Explanation:
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

NEW QUESTION # 118
What is one best practice when designing a UiPath Communications Mining label taxonomy?

A. Each label should be identifiable from the text of the individual verbatim (not thread) to which it will be applied.
B. Each label should include customer experience/sentiment analysis in its coverage.
C. Each parent label should have at least 3 children labels to ensure specificity.
D. Each label should overlap sliqhtlv with a few distinct others so we ensure 100% coveraqe.

Answer: A

Explanation:
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.
References: 1: Communications Mining - Taxonomies 2: Communications Mining - Label hierarchy and best practice

NEW QUESTION # 119
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Leo West Leo West

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