UiPath-ABAv1 Exam Dumps - UiPath Certified Professional Automation Business Analyst Professional v1.0

Definition of Structured Documents:

Structured documents are those with a clear and consistent format, such as invoices, purchase orders, and utility bills, making them suitable for automated processing using Document Understanding.

Options Analysis:

Contracts and Emails (Option A): These are unstructured or semi-structured documents with variable layouts.

Forms, Passports, and ID Cards (Option B): While forms are structured, passports and ID cards are semi-structured, with less consistent formats.

Combination (Option C): Including contracts and emails makes this option incorrect since they do not qualify as structured documents.

Correct Option (D): Invoices, purchase orders, and utility bills have standardized layouts, making them ideal for processing.

Use in UiPath: Document Understanding extracts data from structured documents using OCR and predefined templates, ensuring accuracy in automation​​.

Given that the invoices are coming from a few dozen vendors (and likely have similar formats), the Form-based extractor in UiPath Document Understanding is the best approach for this scenario. The form-based extractor is specifically designed for structured or semi-structured documents that share a consistent layout, like invoices, forms, and other documents with key-value pairs.

"Form Extractor extracts data from structured forms or documents with fixed templates by mapping the field names directly in the document."

This makes the extraction process more robust and accurate because the layout is predictable, ensuring reliable data extraction.

The document shown is an invoice with a consistent layout, known fields (e.g., invoice number, date, line items, shipping & handling, totals). In Document Understanding, such a document is classified as a fixed structured format.

As per UiPath’s definition:

“Fixed structured format documents are documents with a well-defined and unchanging structure, such as invoices, purchase orders, and utility bills.”

These documents are typically the easiest to work with because the location and format of fields do not vary, enabling highly accurate extraction using form-based extractors or pre-trained models.

To train a custom Machine Learning (ML) model that includes one custom extra field, it is recommended to use between 20 to 50 document samples. This range provides a sufficient amount of data to ensure the model can learn effectively while accommodating the variability introduced by the additional custom field​

Processes that are complex yet offer high benefits are seen as long-term improvements. They require more effort and time to automate but promise significant returns, making them strategic investments in automation.