UiPath-SAIAv1 無料問題集「UiPath Specialized AI Associate Exam (2023.10)」

（A）For structured or semi-structured documents where for certain fields, data is always found in a strict, predictable format and context.

（B）For structured or semi-structured documents in which layouts of different document providers vary greatly.

（C）For structured or semi-structured documents where the extraction method is based on a set of FlexiCapture definition files.

（D）For use cases in which non-variable format documents need to be processed.

（A）A model rating of 70-90 or better.

（B）A model rating of 50-60.

（C）A model rating of 30-40.

（D）A model rating of 40-50.

（A）Asset types can be modified after the asset is created.

（B）Asset values can be specified for each user.

（C）Assets can store complete DataTable variables.

（D）All values from any Asset type are encrypted.

（A）It is a pre-trained Machine Learning model that helps you classify documents by providing a prompt.

（B）It allows users to bypass the need for manually creating Datasets, Pipelines, and ML Skills in AI Center and enables training document classifiers directly within Document Understanding.

（C）It enables you to directly edit and alter the underlying code of the ML models used for classification, giving the users unprecedented control over the machine learning process in Document Understanding.

（D）It enables users to manually create Datasets, Pipelines, and ML Skills in Document Understanding itself.

（A）How the top 10 labels for a given time period perform compared to the previous period and their change in rank.

（B）How the top 10 entities for a given time period perform compared to the previous period and their change in rank.

（C）How the top 10 labels and entities for a given time period perform compared to the previous period and their change in rank.

（D）How the top 10 senders for a given time period perform compared to the previous period and their change in rank.

（A）List

（B）Array

（C）Tuple

（D）Integer

（A）3

（B）5

（C）4

（D）2

（A）Name, Classification type. Input to be labeled. Attribute name. Shortcut, and Color.

（B）Name, Classification Type. Attribute name. Color, and Shortcut.

（C）Name, Classification type, Attribute name, and Shortcut.

（D）Name, Input to be labeled. Attribute name, and Shortcut.

（A）The purpose of the End Process in the Document Understanding Process is to generate a summary report of the processing statistics and performance metrics.

（B）End Process is a feature in the Document Understanding Process that exports the extracted data into a readable document format.

（C）End Process in the Document Understanding Process silently shuts down the Virtual Machine so that another robot can use it.

（D）End Process sets the queue transaction status as Successful in case of no exception, and as Failed in case of an exception with their corresponding Business or System Exception, and the post processing
/cleaning if required.

（A）OmniPage.

（B）UiPath Document OCR.

（C）UiPath CJK (for Chinese. Japanese. Korean).

（D）Tesseract OCR.

（A）To convert scanned documents into machine-readable formats, utilizing taxonomy rules to enhance digitized content accuracy.

（B）To automatically extract structured data fields from documents, leveraging taxonomy mappings for precise data extraction.

（C）To define the document types and the pieces of information targeted for data extraction (fields) for each document type.

（D）To assign predefined document categories based on content similarity, simplifying classification tasks for easier document organization.

（A）Remove and kill.

（B）Kill, remove, and restart.

（C）Kill.

（D）Remove and restart.

（A）Generative capabilities for classifying various document types.

（B）Manual classification of document types.

（C）Only using pre-trained extraction models.

（D）Real-time document translation.

（A）PDFs and images.

（B）Images and videos.

（C）Spreadsheets and databases.

（D）Word documents and presentations.

（A）Label predictions are made based on the text of the message as well as some metadata properties, while general fields are learnt from the assigned span of text and the context of the text surrounding that span.

（B）Label predictions rely solely on metadata properties, general fields are learnt from the presence of certain key phrases in the text.

（C）Labels and general fields are both predicted based on the text of the message, with no consideration given to metadata properties or contextual surrounding text.

（D）Labels predictions are based on assigned span of text and entities are predicted solely from metadata properties.

（A）Languages using the Cyrillic alphabet, the Greek left-to-right alphabet, and Chinese.

（B）Languages using the Latin alphabet, the Cyrillic alphabet, the Greek left-to-right alphabet. Japanese, and Chinese.

（C）Languages using the Greek left-to-right alphabet. Japanese, and Chinese.

（D）Languages using the Latin alphabet (like Italian, French. Portuguese. Spanish, and Romanian), and the Greek left-to-right alphabet.

（A）eval.use_cuda

（B）eval.enable_ocr

（C）eval.enable_qpu

（D）eval.redo_ocr