CUSTOMIZABLE UIPATH UIPATH-SAIV1 PRACTICE TEST SOFTWARE

Customizable UiPath UiPath-SAIv1 Practice Test Software

Customizable UiPath UiPath-SAIv1 Practice Test Software

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q39-Q44):

NEW QUESTION # 39
When processing a document type that comes in a high variety of layouts, what is the recommended data extraction methodology?

  • A. Hybrid data extraction.
  • B. Model-based data extraction.
  • C. Manual data extraction.
  • D. Rule-based data extraction.

Answer: A

Explanation:
Based on the classification of documents, there are two common types of data extraction methodologies: rule- based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor's internal taxonomy (if any), and how to implement "fall-back" rules for data extraction2.
References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction


NEW QUESTION # 40
Having the following list of documents:
Invoice1.pdf, Invoice2.raw, Invoice3.gif, Invoice4.jpg, Invoice5.docx
Please choose all the files that can be used in the DocumentPath property of the Classify Document Scope activity.

  • A. Invoice1.pdf, Invoice4.jpg, Invoice5.docx
  • B. Invoice1.pdf, Invoice3.gif, Invoice5.docx
  • C. Invoice1.pdf, Invoice3.gif, Invoice4.jpg
  • D. Invoice1.pdf, Invoice2.raw, Invoice3.gif, Invoice4.jpg

Answer: C

Explanation:
The Classify Document Scope activity in UiPath is used to classify documents supported by the Document Understanding framework. It primarily works with file formats like PDF, JPG, PNG, and other image-based formats but does not process raw or non-standard file types like .raw.


NEW QUESTION # 41
Which of the following OCR (Optical Character Recognition) engines is not free of charge?

  • A. Microsoft OCR.
  • B. OmniPaqe.
  • C. Microsoft Azure OCR.
  • D. Tesseract.

Answer: B

Explanation:
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 200 languages. 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.
References:
* Document Understanding - OCR Engines
* Automation Pricing - Complete UiPath Enterprise Solution


NEW QUESTION # 42
What is the difference in scope between variables and arguments in UiPath?

  • A. Variables have a limited scope within a specific workflow or sequence, while arguments are used to pass data between workflows.
  • B. Variables are global and arguments are local.
  • C. Variables can only be used within the main workflow, while arguments can only be used within invoked workflows.
  • D. Variables are used for input data, while arguments are used for output data.

Answer: A

Explanation:
In UiPath:
* Variables are scoped to a specific workflow or sequence and are used to store data for operations within that scope.
* Arguments, on the other hand, are used to pass data between workflows, allowing information to flow in or out of invoked workflows.


NEW QUESTION # 43
In the general fields training, what actions does the Communications Mining Train feature guide you through?

  • A. It guides you to focus solely on model refinement.
  • B. It guides you through the completion of model training before starling entity training.
  • C. It guides you right from the moment you create a dataset with the next best action to take to advance your general fields training.
  • D. It guides you to start entity training without a dataset.

Answer: C

Explanation:
his is confirmed in the UiPath documentation, where the "Train" feature guides users from the dataset creation stage through the necessary steps to optimize the training process. The system recommends next best actions based on progress to ensure efficient and focused training


NEW QUESTION # 44
......

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