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

1. Why would you choose the Argument input method for an activity field?

A) Lets the agent infer the field value at runtime using the Description and its reasoning.
B) Receives a runtime value from an agent input argument defined earlier in the workflow.
C) Applies one constant value you enter during design every time the agent executes the activity.
D) Prompts a person to supply the value each time the field is evaluated at runtime.

2. 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 {{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.
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 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.

3. How does adjusting the "Number of results" setting affect the agent's use of context from indexes?

A) It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
B) It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
C) It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.
D) It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.

4. You are building an agent that classifies incoming emails into one of three categories: Urgent, Normal, or Spam. You want to improve accuracy by using few-shot examples in a structured format. Which approach best supports this goal?

A) Show one example and leave the label blank for inference.
B) Use examples such as:
Input: "Please address this issue immediately, server is down!" Output: "Urgent"
C) Include three random emails and let the LLM guess the intent.
D) Use unlabeled prompts followed by ranked categories:
Classify this. "Need update on report." - [1] Urgent [2] Normal [3] Spam

5. 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) Use random and unrelated examples to test the prompt's robustness.
D) Choose examples that are diverse, relevant, and typical of the task's expected input.

Solutions: