RELIABLE UIPATH-SAIV1 EXAM SYLLABUS, UIPATH-SAIV1 NEW PRACTICE QUESTIONS

Reliable UiPath-SAIv1 Exam Syllabus, UiPath-SAIv1 New Practice Questions

Reliable UiPath-SAIv1 Exam Syllabus, UiPath-SAIv1 New Practice Questions

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q36-Q41):

NEW QUESTION # 36
What is the order of steps for automatically retraining and deploying a Document Understanding ML Model in Al Center with data from Document Validation Action?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

Explanation:

To automatically retrain and deploy a Document Understanding Machine Learning (ML) Model in AI Center with data from the Document Validation Action, the steps should be followed in this order:
* Send Human in the Loop data to the used Dataset.
* This step involves sending the data that has been validated and corrected by human reviewers to the dataset. This data will be used for training the ML model.
* Use the Export feature from Document Manager using Scheduled Export.
* After the data is reviewed and validated, it needs to be exported from the Document Manager.
Scheduled Export automates this process, ensuring the dataset in AI Center is regularly updated with new data.
* Run Training/Full Pipeline in AI Center using Scheduled Pipeline with Auto-Retraining enabled.
* With the updated data in the dataset, the next step is to run the training or the full pipeline. The use of Scheduled Pipeline with Auto-Retraining ensures that the ML model is automatically retrained with the latest data.
* Enable the Auto Update feature in the ML Skill.
* Finally, enabling the Auto Update feature in the ML Skill ensures that the newly trained model is automatically deployed, making the improved model available for document understanding tasks.
Following these steps in the specified order allows for a streamlined process of continuously improving the ML model based on human-validated data, ensuring better accuracy and efficiency in document understanding tasks over time.


NEW QUESTION # 37
Which of the following extractors can be used for Data Extraction Scope activity?

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

Answer: A

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 # 38
What new capability has been introduced for processing unstructured documents in the 2023.10 release?

  • A. Manual classification of document types.
  • B. Real-time document translation.
  • C. Generative capabilities for classifying various document types.
  • D. Only using pre-trained extraction models.

Answer: C

Explanation:
In the 2023.10 release of UiPath, a significant new feature is the introduction of generative capabilities for classifying various document types. This update allows for more advanced handling of unstructured data by leveraging AI to automatically generate classifications, even for document types that the model hasn't encountered before. This feature enhances the flexibility and power of Document Understanding, enabling organizations to handle a wider variety of documents without needing extensive manual setup for each new type.
(Source: UiPath 2023.10 Release Notes)


NEW QUESTION # 39
For what type of documents is it recommended to use the Form Extractor?

  • A. For use cases in which non-variable format documents need to be processed.
  • B. For structured or semi-structured documents where the extraction method is based on a set of FlexiCapture definition files.
  • C. For structured or semi-structured documents where for certain fields, data is always found in a strict, predictable format and context.
  • D. For structured or semi-structured documents in which layouts of different document providers vary greatly.

Answer: A

Explanation:
The Form Extractor in UiPath is best suited for documents that have a fixed or non-variable format. This means that documents where the layout remains consistent and predictable, such as standardized forms or invoices, are ideal for this extraction method. The Form Extractor uses predefined templates that map data fields based on their positions, making it highly effective for extracting data from documents with little to no variation in layout. It is not well-suited for documents with significant layout variability, which would require a more flexible extraction approach, such as a machine learning extractor.
(Source: UiPath Document Understanding documentation)


NEW QUESTION # 40
What does a UiPath Communications Mining taxonomy include?

  • A. Messages, labels, and general fields.
  • B. General fields and datasets.
  • C. Labels and general fields.
  • D. Labels and sources.

Answer: C


NEW QUESTION # 41
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