UiPath UiPath-AAAv1 Dumps Questions Answers

Ais the correct andUiPath-recommended approach:

RPA botsare ideal forstructured, rule-based, high-volume tasks— like data entry, file manipulation, system integration — wherepredictability and speedare key.

Agentic AIexcels inunstructured, human-like decision scenarios — likeinterpreting emails,triaging support requests, orresponding to exceptionsusing LLMs and contextual memory.

UiPath promotes ahybrid automation model:

Letrobotshandle deterministic workflows.

Letagentsmanage ambiguity, natural language, and decision-making.

Lethumanshandle escalations or approvals when required.

This createsscalable, intelligent, and efficientworkflows that combine strengths from both systems.

B and C are incorrect because neither agents nor bots alone are sufficient across all use cases.

D reverses the design logic — agents arenotbest for structured tasks; RPA is.

This hybrid approach is foundational in UiPath’sAgentic Orchestration and Co-Pilotstrategies, ensuringright-tool-for-the-taskautomation at scale.

Dis correct — in UiPath agent design, when adding anescalation, a key step is to provide aclear and contextual promptin theProperties panelthat tells the agentwhen and whyto trigger that escalation.

This prompt:

Informs the LLM of thebusiness logicbehind escalation

Sets thethresholds or exception casesthat warrant human review

Ensures escalation is usedintelligently and selectively

For example:

"If the customer expresses dissatisfaction and refund amount exceeds $500, escalate to supervisor."

This guidance is crucial becauseagents rely on prompts to decide, not just flow logic. Without a well-written prompt, the LLM may over-escalate or miss critical cases.

Option A is partially correct, butoutcome behaviorconfigureswhat happens after escalation— notwhen to trigger it.

B skips the logic layer entirely.

C refers to field requirements but doesn’t influence agentdecision-making logic.

The prompt within the escalation tool is where theLLM’s judgment gets guided, making D the essential step for enabling smart, situational escalations.

Dis correct — includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.

UiPath encourages the use of examples for:

Classification(e.g., labeling sentiment, email categories)

Transformation tasks(e.g., turning unstructured text into tables)

Step-by-step instructions(e.g., troubleshooting flows)

These examples serve two purposes:

Pattern induction: The model picks up on consistent structures or rules used across examples.

Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.

Best practice:

Usetypical, real-world examplesrepresentative of the data the agent will encounter.

Keep formatsclear and consistentacross input-output pairs.

Pair examples withexplicit instructionsin the system or user prompt.

Option A is flawed — focusing only on edge cases can confuse the model.

B is false — omitting examples forces the LLM to guess the structure, reducing accuracy.

C is misleading — examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.

In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.