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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:

1. 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.

2. When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?

A) When you plan to paste several different tables in succession during the same chat and expect Autopilot for Everyone to queue each paste automatically.
B) When you are using macOS and want Autopilot for Everyone to perform a copy and paste on a Linux VM.
C) Whenever you need to paste any content regardless of operating system, file type, or the number of pastes.
D) When you are working on a Windows machine and need to perform a single AI-powered paste of a table (for example, from a PDF) into another application directly from the chat interface.

3. An agent is being designed to generate step-by-step troubleshooting guides for software issues. Testing shows that the guides lack clarity and include redundant steps, confusing users. What is the best refinement for the prompt?

A) Avoid explaining each step in detail to simplify the prompt.
B) Enable the agent to generate longer troubleshooting guides for completeness.
C) Provide clear instructions to make steps actionable, concise, and free of redundancies.
D) Add generic examples to allow the agent to experiment with the step format.

4. What are the characteristics of an agentic story within the 'Do later' quadrant in the impact and feasibility matrix?

A) Low feasibility and High Impact
B) Low feasibility and Low Impact
C) High feasibility and High Impact
D) High feasibility and Low Impact

5. What is one of the key benefits of providing RAG as a service to UiPath generative AI experiences?

A) It eliminates the need for knowledge bases by integrating all proprietary data directly into generative applications.
B) It exclusively provides access to historical data sources without supporting real-time updates.
C) It reduces the risk of hallucination by referencing ground truth data stores.
D) It directly increases the LLM context window size without any interaction with knowledge bases.

Solutions: