A financial services firm is using UiPath Communications Mining to analyze customer emails. A model has been in production for six months with a 'Good' rating. Recently, the 'Balance' performance factor has dropped to 'Poor', while 'Performance' and 'Coverage' remain 'Good'. This is causing the model to over-predict common requests and miss newer, less frequent issues. What is the most appropriate first action in the 'Validation' page to address this specific problem?
A developer is building a Document Understanding process using the DU Process Template. The requirement is to prevent any documents larger than 25 MB or with more than 100 pages from being processed to avoid excessive AI Unit consumption and potential timeouts. Where should this validation logic be implemented according to the template's best practices?
An automation architect needs to deploy a custom ML model for sentiment analysis, which was developed by an in-house data science team and packaged as a Docker container. The model is not part of the standard UiPath ML Packages. Which actions must be performed in AI Center to make this model available as an ML Skill? (Select TWO)
True or False: In the UiPath Document Understanding Process template, the `ShouldStop` variable is exclusively used to gracefully stop the robot from Orchestrator and has no other function within the framework's logic.
A project requires extracting data from scanned, historical engineering diagrams. The text is often hand-written, uses non-standard fonts, and is sometimes skewed. The primary goal is to achieve the highest possible accuracy in text recognition before classification and extraction. Which OCR engine should be prioritized for this use case?
A consultant is designing a Communications Mining taxonomy for an insurance company to analyze claims emails. The primary goal is to automate the initial routing and data entry for new claims. Which of the following represents the best-practice approach for designing the label hierarchy for this automation-focused use case?
## Case Study: Global Logistics Document Automation **Company Background:** Global Transport Inc. (GTI) is a multinational logistics company that processes thousands of shipping documents daily, including Bills of Lading (BOL), Commercial Invoices, and Packing Lists. These documents arrive from hundreds of different partners in various formats, ranging from high-quality structured PDFs to skewed, low-resolution scans. The data from these documents is manually entered into their Transportation Management System (TMS), leading to significant delays, data entry errors, and high operational costs. **Current Situation:** GTI has initiated a project to automate this process using UiPath Document Understanding. The project team has successfully created a taxonomy and is now in the process of designing the core DU workflow. A key challenge is the high variability in document quality and layout. The same partner might send a clear, machine-readable invoice one day and a poorly scanned, handwritten BOL the next. The system must be resilient and require minimal human intervention for standard documents but also robustly handle exceptions. **Requirements:** 1. The solution must classify each incoming document as either a BOL, Commercial Invoice, or Packing List. 2. For documents that cannot be classified with high confidence, a human user must be prompted for manual classification. 3. The solution must use a combination of rule-based and model-based extractors to maximize accuracy across different document layouts. 4. All extracted data must be validated against a set of business rules (e.g., 'Total Amount' must equal the sum of line items). Data that fails validation must be sent to a human for correction in Action Center. 5. The final, validated data must be exported as a JSON file for ingestion into the TMS. Which workflow design most effectively meets all of GTI's requirements?
A developer is using the `UiPath Communications Mining` activities package in Studio. They need to process emails from a stream, but only those that have a predicted label of 'Urgent Inquiry' with a confidence score of 85% or higher. Which activity and property combination should be used to achieve this?
During the 'Refine' phase of Communications Mining model training, an analysis of the 'Validation' page reveals the following metrics for a label named 'Address_Change': - Precision: 95% - Recall: 40% - F1 Score: 57% What does this combination of metrics indicate about the model's performance for this label, and what is the recommended training action?
A developer has configured a Document Understanding solution where extracted data is sent to Action Center for validation. A business requirement is to add a note for the human validator, explaining a specific business rule for the 'Invoice_Total' field. How can this be achieved?