UIPATH-SAIAV1 LATEST BRAINDUMPS PDF - VALID UIPATH-SAIAV1 TEST SIMS

UiPath-SAIAv1 Latest Braindumps Pdf - Valid UiPath-SAIAv1 Test Sims

UiPath-SAIAv1 Latest Braindumps Pdf - Valid UiPath-SAIAv1 Test Sims

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q103-Q108):

NEW QUESTION # 103
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. Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
  • B. Consider the document types, language, and data quality. It is important to select one that is specifically trained or optimized for the document types being processed. It is also important to takeinto account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
  • C. The popularity of the ML Extractor among other UiPath users should be the primary factor. Opt for the ML Extractor that has the highest number of downloads or positive reviews.
  • D. The size of the ML Extractor is the most important factor to consider. 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.

Answer: B

Explanation:
The best practice is to select an ML Extractor based on document types, language, and data quality. Choosing a model specifically optimized for the type of document being processed ensures higher accuracy and reliability. The quality and diversity of the training data used to develop the model play a significant role in its performance.
Reference: UiPath ML Extractors


NEW QUESTION # 104
In UiPath Communications Mining, what does the Reports section contain?

  • A. Tools for evaluating model performance.
  • B. Tools for message analytics and monitoring.
  • C. Tools for evaluating label and entity performance.
  • D. Tools for comparing different model versions.

Answer: B

Explanation:
In UiPath Communications Mining, the Reports section provides a variety of tools for analyzing and monitoring messages within a dataset. This includes functionalities like label summaries, trends, and message segmentation, which allow users to gain insights into message volumes, sentiment trends, and other analytical metrics. The Reports section is integral for tracking the performance of messages and the overall dataset, making it useful for monitoring communication channels


NEW QUESTION # 105
If you need to retrieve an item based on a corresponding identifier in UiPath, which collection type would you use?

  • A. System.Collections.Generic.Dictionary<TKey, TValue> as it stores key-value pairs
  • B. System.Collections.Generic.List<T> as it stores ordered values
  • C. System.Collections.Generic.Dictionary<TKey, TValue> as it stores indexed values
  • D. ArrayOf<T> or System.DataType[] as it stores indexed values

Answer: A

Explanation:
Reference: UiPath Dictionaries


NEW QUESTION # 106
What components are part of the Document Understanding Process template?

  • A. Import. Classification. Text Extractor, and Data Validation.
  • B. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.
  • C. Load Document. Categorization. Data Extraction, and Validation.
  • D. Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.

Answer: D

Explanation:
The Document Understanding Process template is a fully functional UiPath Studio project template based on a document processing flowchart. It provides logging, exception handling, retry mechanisms, and all the methods that should be used in a Document Understanding workflow, out of the box. The template has an architecture decoupled from other connected automations and supports both attended and unattended processes with human-in-the-loop validation via Action Center. The template consists of the following components1:
Load Taxonomy: This component loads the taxonomy file that defines the document types and fields to be extracted. The taxonomy file can be created using the Taxonomy Manager in Studio or the Data Manager web application.
Digitization: This component converts the input document into a digital format that can be processed by the subsequent components. It uses the Digitize Document activity to perform OCR (optical character recognition) on the document and obtain a Document Object Model (DOM).
Classification: This component determines the document type of the input document using theClassify Document Scope activity. It can use either a Keyword Based Classifier or a Machine Learning Classifier, depending on the configuration. The classification result is stored in a ClassificationResult variable.
Data Extraction: This component extracts the relevant data from the input document using the Data Extraction Scope activity. It can use different extractors for different document types, such as the Form Extractor, the Machine Learning Extractor, the Regex Based Extractor, or the Intelligent Form Extractor. The extraction result is stored in an ExtractionResult variable.
Data Validation: This component allows human validation and correction of the extracted data using the Present Validation Station activity. It opens the Validation Station window where the user can review and edit the extracted data, as well as provide feedback for retraining the classifiers and extractors. The validated data is stored in a DocumentValidationResult variable.
Export: This component exports the validated data to a desired output, such as an Excel file, a database, or a downstream process. It uses the Export Extraction Results activity to convert the DocumentValidationResult variable into a DataTable variable, which can then be manipulated or written using other activities.
References: Document Understanding Process: Studio Template, Document Understanding Process - New Studio Template, Document Understanding Process Template in UiPath Studio


NEW QUESTION # 107
What does the darker shading of a label prediction represent in Explore in UiPath Communications Mining?

  • A. Multiple label predictions.
  • B. A higher confidence score.
  • C. An incorrect prediction.
  • D. A lower confidence score.

Answer: B

Explanation:
In UiPath Communications Mining, the shading of label predictions in the Explore section indicates confidence levels. Darker shading represents a higher confidence score, meaning the model is more certain of the prediction made for that particular label


NEW QUESTION # 108
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