A. Sums up the subtotal to the total variable by converting it to CDec

B. Sums up all the line amounts converted to CDec and the subtotal

C. Sums up all the line amounts and converts the fields to CDec.

D. Sums up subtotal fields from the config file converted to CDec with the subtotal.

A. A solution for the processing of Excel files for extracting data tables.

B. A solution for combining different approaches to extract information from workflows.

C. A solution that offers the ability to digitize, extract, validate, and train data from documents.

D. A solution for combining different approaches to extract entities from Word documents such as contracts and agreements.

A. For a given concept what % of cases will the model correctly predict.

B. For a given concept, what % of cases will the model not detect.

C. For a given concept what % of cases will the model incorrectly predict.

D. For a given concept, what % of cases will the model detect.

A. Invoice1.pdf, Invoice3.gif, Invoice4.jpg

B. Invoice1.pdf, Invoice3.gif, Invoice5.docx

C. Invoice1.pdf, Invoice2.raw, Invoice3.gif, Invoice4.jpg

D. Invoice1.pdf, Invoice4.jpg, Invoice5.docx

A. Digitized Object Model is an XML object containing information such as page data, content, and coordinates for every image identified in the file.

B. Digitize Object Module is an XML object containing information such as mandatory field names, types, and values

C. Data Object Model is a YAML object containing information such as name, content type, text length, and the number of pages.

D. Document Object Model is a JSON object containing information such as name, content type, text length, and the number of pages.

A. Digitizes the document that should be processed.

B. Identifies words and their coordinates from images and PDFs.

C. Identifies and extracts specific information that should be processed.

D. Applies rules for validating that the information extracted from a document is correct.

A. To select which extractors are trained for each document type and field.

B. To create and edit a Taxonomy file specific to the current automation project.

C. To present a document processing specific user interface for validating and correcting automatic classification outputs.

D. To select the type of ML that can be used in the project.

A. UiPath Task Mining.

B. UiPath Process Mining.

C. UiPath Communications Mining.

D. UiPath Document Understanding.

A. All of the labels in the dataset have at least 25 training examples.

B. 2% of the dataset has been reviewed by a model trainer.

C. The model makes correct predictions across the majority of the dataset.

D. Many of the labels in the taxonomy have amber or red performance warnings.

A. Administrator.

B. Data Scientist.

C. RPA Developer.
D Process Controller

A. Pre-trained. custom training, and fine-tunable.

B. Custom training, fine-tunable, and reviewed.

C. Pre-trained. fine-tunable, and reviewed.

D. Pre-trained, custom training, and reviewed.