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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q97-Q102):

NEW QUESTION # 97
What is the recommended split of documents for training and evaluation, considering a total of 15 documents per vendor?

A. 8 documents for training the model, and 7 for evaluating the model.
B. 12 documents for training the model, and 3 for evaluating the model.
C. 7 documents for training the model, and 8 for evaluating the model.
D. 10 documents for training the model, and 5 for evaluating the model.

Answer: D

NEW QUESTION # 98
What is the correct execution order of the Document Understanding template stages?
Instructions: Drag the stages found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

Explanation:
The correct execution order of the Document Understanding template stages is:
* Taxonomy
* Digitize
* Classify
* Extract
* Extraction Validation
* Export
Comprehensive and Detailed Explanation: The Document Understanding template stages are based on a document processing flowchart that follows these steps:
* First, you need to define the Taxonomy of the document types and fields that you want to process and extract information from. This is done using the Taxonomy Manager in UiPath Studio1.
* Next, you need to Digitize the input documents, which can be in various formats such as PDF, image, or text. This is done using the Digitize Document activity, which converts the documents into a machine-readable format and performs OCR if needed2.
* Then, you need to Classify the digitized documents into the predefined document types in your taxonomy. This is done using the Classify Document Scope activity, which can use various classifiers such as Keyword Based Classifier, Machine Learning Classifier, or Intelligent Form Extractor3.
* After that, you need to Extract the relevant information from the classified documents based on the fields in your taxonomy. This is done using the Data Extraction Scope activity, which can use various extractors such as Regex Based Extractor, Machine Learning Extractor, or Form Extractor.
* Next, you need to perform Extraction Validation to review and correct the extracted information, either manually or automatically. This is done using the Present Validation Station activity, which can use either the Validation Station or the Action Center for human-in-the-loop validation.
* Finally, you need to Export the validated information to the desired output location, such as a file, a database, or a queue. This is done using the Export Extraction Results activity, which can use various exporters such as Excel Exporter, CSV Exporter, or Queue Item Exporter.
References:
* UiPath Studio - Taxonomy Manager
* UiPath Activities - Digitize Document
* UiPath Activities - Classify Document Scope

NEW QUESTION # 99
What additional information can be included in the exported data, apart from the extraction results?

A. The position on the page.
B. The extraction confidence and the digitization confidence.
C. The number of occurrences and the extraction confidence.
D. The page number from which the field was extracted and the exact position on the page.

Answer: D

Explanation:
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.
References: Document Understanding Process: Studio Template, Export Results

NEW QUESTION # 100
Which of the following extractors can be used for Data Extraction Scope activity?

A. Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent Form Extractor
B. Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and Regex Based Extractor.
C. Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and Machine Learning Extractor.
D. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.

Answer: C

Explanation:
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

NEW QUESTION # 101
How does Al Center handle versioning of ML packages?

A. Only ML packages retrained via pipelines change their minor version.
B. Only ML packages uploaded by users change their minor version.
C. Only ML packages retrained via pipelines change their major version.
D. Versioning is not supported for ML packages.

Answer: A

Explanation:
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).

NEW QUESTION # 102
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Pass Guaranteed Quiz 2025 Professional UiPath UiPath-SAIv1: Study UiPath Certified Professional Specialized AI Professional v1.0 Plan

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q97-Q102):

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