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UiPath Agentic Automation Associate UiAAAv1 Dumps in PDF

Free UiPath UiAAAv1 Real Questions (page: 4)

UiAAAv1 PDF — 176 Questions

While configuring an index that points to a sales-ledger.csv file, you need the agent to answer SQL-style questions such as "total revenue per region".
Which query type setting in the Contexts panel will make Context Grounding treat the CSV as tabular data and enable cell-level look-ups?

  1. Semantic
  2. Decrease relevance score threshold
  3. Let the agent infer
  4. Structured

Answer(s): D

Explanation:

Selecting the Structured query type ensures Context Grounding interprets the CSV as tabular data, allowing SQL-style queries and precise cell-level lookups such as calculating total revenue per region.



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

  1. You are an invoice approver. After processing, output exactly the following JSON template:
    { "id": "ABC-123", "status": "approved", "amount": 9999.99 } 1 Extract {{Invoice_ID}} from the email text.
    2 When an {{Invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
    3 If the total $10 000, escalate the case to Finance in Action Center, sending {{Invoice_ID}}, amount, and supplier.
    4 If the total < $10 000, approve the invoice.
    Populate the fields above with real data.
  2. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.

    1 Extract Invoice_ID from the email text.
    2 When an Invoice_ID is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
    3. If the total $10 000, escalate the case to Finance in Action Center, sending Invoice_ID, amount, and supplier.
    4 If the total < $10 000, approve the invoice.
    5 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.
  3. You are an invoice-approval agent who deals only with supplier invoices and rejects any other request.
    1 Extract {{Invoice_ID}} from the email text.
    2 When an {{Invoice_ID}} is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
    3 If the total $10 000, escalate the case to Finance in Action Center, sending {{Invoice_ID}}, amount, and supplier.
    4 If the total < $10 000, approve the invoice.
    5 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.
  4. You are an invoice approver. After processing, output exactly the following JSON template:
    { "id": "ABC-123", "status": "approved", "amount": 9999.99 } 1 Extract Invoice_ID from the email text.
    2 When an Invoice_ID is found, run the LookupInvoice tool to retrieve invoice amount and supplier name.
    3 If the total $10 000, escalate the case to Finance in Action Center, sending Invoice_ID, amount, and supplier.
    4 If the total < $10 000, approve the invoice.
    Populate the fields above with real data.

Answer(s): C

Explanation:

Chosen draft follows UiPath guidance by clearly defining context (agent role and scope), instructions (step-by- step extraction and decision logic), output format (structured <invoice_status> tags), and constraints (rejecting non-invoice tasks, concise professional tone).



What configuration options are available for escalations to ensure agents use them effectively?

  1. You can assign a single escalation to one agent.
  2. Agents can use multiple escalations automatically without any need for pre-configuration or context-specific descriptions.
  3. Escalations can use an Action app with a unique name and description to clarify specific circumstances.
  4. Escalation components cannot use unique Action app names or descriptions.

Answer(s): C

Explanation:

Escalations can be configured with an Action app that includes a unique name and description. This ensures agents understand the specific context in which to use the escalation, improving effectiveness and clarity.



What is the role of indexes in UiPath Agents?

  1. To schedule agent tasks
  2. To manage automation package versions
  3. To provide agents access to permissioned knowledge bases
  4. To monitor agent performance

Answer(s): C

Explanation:

In UiPath Agents, indexes serve as connectors to permissioned knowledge bases, enabling agents to retrieve and ground their responses in relevant, authorized data sources.



When passing runtime data into an Agent, which approach ensures the input argument is actually available inside the user prompt at execution time?

  1. Declare the argument in the system prompt; any text surrounded by angle brackets (e.g., <CUSTOMER_EMAIL>) will be substituted automatically.
  2. Use single braces like {CUSTOMER_EMAIL}, because the platform automatically normalizes the identifier.
  3. Simply mention the variable name in plain prose- the Agent will infer the value from the workflow without special syntax.
  4. Create the argument in Data Manager and reference it verbatim inside double curly braces e.g.
    {{CUSTOMER_EMAIL}}, so the name matches exactly.

Answer(s): D

Explanation:

To ensure runtime data is passed correctly, the argument must be created in Data Manager and referenced with double curly braces (e.g., {{CUSTOMER_EMAIL}}). This guarantees the value is substituted into the user prompt at execution.



What should be prepared for workshops aimed at identifying agentic automation opportunities?

  1. A list of potential agents and technical prototypes ready for deployment.
  2. Comprehensive automation implementation plans for all roles
  3. A completed "to-be" process map tailored to automation goals.
  4. Adjusted or annotated "as-is" process maps to highlight inefficiencies and automation potential.

Answer(s): D

Explanation:

For workshops, adjusted or annotated "as-is" process maps should be prepared. These maps highlight inefficiencies and pain points, helping participants identify realistic agentic automation opportunities.



Which situation is an appropriate reason for an agent to raise an escalation during execution?

  1. The agent has just started its first step
  2. The Agent Memory time-to-live reaches three months
  3. A tool call returns data with an unexpected structure
  4. The agent successfully finishes every planned step

Answer(s): C

Explanation:

An escalation should be raised when the agent encounters an unexpected situation it cannot resolve on its own, such as a tool call returning data with an unexpected structure. This ensures human intervention handles exceptions appropriately.



Which of the following best illustrates how to optimize prompt engineering when dealing with domain-specific tasks?

  1. Provide sufficient context within the prompt to convey nuances and background information relevant to the domain.
  2. Focus exclusively on iterative refining of the prompt without considering the complexity of the specific domain.
  3. Use multiple examples, even if they are outside the domain, to help the model generalize better across tasks.
  4. Rely solely on simple, clear prompts without providing additional details, regardless of task complexity.

Answer(s): A

Explanation:

For domain-specific tasks, optimizing prompt engineering requires providing sufficient context in the prompt to capture domain nuances and background information, ensuring the model generates accurate and relevant outputs.



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AI Tutor Explanation
7/1/2026 9:25:07 AM

Question 1:
The correct answer is C.
Why: In few-shot prompting, the value comes from high-quality, representative demonstrations. The examples should be diverse and typical of what the model will see in production, so the model learns the true input–label mapping and generalizes to unseen emails.
Why the other options are less appropriate:

  • A: Using random, unrelated examples does not reflect the actual task distribution and won’t help the model generalize to real inputs.
  • B: “Always use more than 10 examples” isn’t a universal rule; quantity without quality and relevance can add noise.
  • D: Intentionally incorrect labels would mislead the model and degrade performance; you want correct, coherent mappings.

Practical tip: ensure the examples cover common cases and edge cases, use the same input–output format, and keep labels consistent with the task (e.g., Spam vs. Work).

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