What is the primary recommendation for usinghttps://www.google.com/search?q=bpmn.uipath.comto access the Maestro modeling canvas?

• A. Utilizehttps://www.google.com/search?q=bpmn.uipath.comexclusively for creating agentic processes with implementation capabilities.
• B. Usehttps://www.google.com/search?q=bpmn.uipath.comonly to import predefined process templates from Studio Web.
• C. Usehttps://www.google.com/search?q=bpmn.uipath.comas a sandbox since the tool does not offer implementation capabilities.
• D. Always usehttps://www.google.com/search?q=bpmn.uipath.comas the primary tool for modeling processes with full implementation features.

Four draft system prompts are shown for an invoice-approval agent. Based on UiPath guidance for context, instruments, and output format constraints, which draft is the most robust choice?

• A. You are an invoice approver. After processing, output exactly the following JSON template:
  { "id": "ABC-123", "status": "approved", "amount": 9999.9 }
  Extract the {{invoice_ID}} from the email text.
  Use LookupInvoice to get the invoice amount and supplier name.
  Escalate to Finance if amount # $10,000.
  If amount > $10,000, approve the invoice.
  Populate the fields above with real data.
• B. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.
  Extract invoice_ID from the email text.
  When an invoice_ID is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
  If the total # $10,000, escalate the case to Finance in Action Center, sending invoice_ID, amount, and supplier.
  If the total > $10,000, approve the invoice.
  Return a reply wrapped inside invoice_status: tags: use <approved> or <awaiting_review> as appropriate.
  Follow a concise, professional tone and refuse tasks outside invoice approval.
• C. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.
  Extract {{invoice_ID}} from the email text.
  When an {{invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
  If the total # $10,000, escalate the case to Finance in Action Center, sending {{invoice_ID}}, amount, and supplier.
  If the total > $10,000, approve the invoice.
  Return a reply wrapped inside invoice_status: tags: use <approved> or <awaiting_review> as appropriate.
  Follow a concise, professional tone and refuse tasks outside invoice approval.
• D. You are an invoice approver. After processing, output exactly the following JSON template:
  { "id": "ABC-123", "status": "approved", "amount": 9999.9 }
  Extract {{invoice_ID}} from the email text.
  When an {{invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
  If the total # $10,000, escalate the case to Finance in Action Center, sending {{invoice_ID}}, amount, and supplier.
  If the total > $10,000, approve the invoice.
  Populate the fields above with real data.

A developer is implementing a few-shot structured prompt for an email classification task. The prompt includes examples of email subjects labeled with their respective classifications, such as "Spam" or "Work." What is the most important aspect to consider when selecting examples for the prompt?

• A. Always use more than 10 examples, regardless of task complexity.
• B. Include examples with intentionally incorrect labels to improve training.
• C. Choose examples that are diverse, relevant, and typical of the task's expected input.
• D. Use random and unrelated examples to test the prompt's robustness.

What are the primary benefits of Context Grounding when querying data across multiple documents?

• A. Context Grounding only extracts random sentences without contextual understanding.
• B. Context Grounding understands relationships between data points across documents, enabling tasks like summarization, data comparison, and retrieval of highly relevant information.
• C. Context Grounding is limited to querying within a single document at a time.
• D. Context Grounding requires manual intervention for identifying connections between data points across documents.