UiPath-AAAv1 Dumps With Exact Questions and Answers

The correct answer isC— the most critical aspect of designing a few-shot prompt in UiPath'sLLM-driven agent frameworkis selecting examples that arediverse,representative, andrelevantto the actual data the agent will encounter in production.

In afew-shot structured prompt, examples are used to demonstrate a pattern the model should follow. UiPath recommends:

Usingrealistic examplesfrom actual user inputs or support tickets

Coveringedge casesor variations in phrasing and tone

Matching thedesired output structureexactly (e.g., Input: ..., Output: ...)

These patterns help the LLMinfer the task correctlyandmaintain consistency, especially when processing unstructured inputs like email subjects.

Option A is incorrect — introducing incorrect labels degrades performance and adds confusion.

B is wrong — the number of examples depends on thetask complexity and token budget. Sometimes 3–5 is ideal.

D undermines task alignment — random examples reduce accuracy and coherence.

UiPath'sPrompt Engineering best practicesprioritizegrounded, contextually rich inputs, particularly when automating classification tasks like spam detection, triage, or intent recognition. High-quality, task-aligned examples lead tomore reliable, human-like agents.

Cis the correct answer — the"Start and wait for external agent"feature in UiPath Maestro is used toinvoke another agentthat has been configured within thesame project or automation environment.

This enables:

Agent-to-agent chaining

Modular designwhere complex tasks are offloaded to specialized agents

Return of results or outputs, once the external agent completes its task

Agents must be:

Properly configured

Input/output ready

Available within the orchestration context of the same solution

Option A is incorrect — this feature is about agents, not robots.

B is wrong — external platforms like Salesforce are accessed via connectors,not as agents.

D is false — input/output parameters can and often should be used between agents.

Dis correct —Context Groundingin UiPath usessemantic search across indexed contentto provide relevant and meaningful context to the agent, even when the data spansmultiple documents.

This capability is powered by:

Embedding-based similarity search(e.g., cosine similarity)

Intelligent chunking and indexing of enterprise data

Runtime query matching based on theagent’s prompt or user input

This enables agents to:

Retrieverelevant information across distributed content

Detectrelationships between topics, even if data is fragmented

Supportmulti-document summarization,comparison, andknowledge-based reasoning

For example, an agent could compare policy details across multiple HR documents to generate a unified response or identify inconsistencies in invoice records spread across different files.

Option A is false —Context Grounding is automaticonce indexing is configured.

B is incorrect — it's explicitly designed toquery across documents.

C misrepresents the system — it doesn’t extract random text; it retrievessemantically relevantpassages based on the LLM's intent.

This powerful grounding mechanism makes UiPath agentsintelligent, context-aware, and enterprise-ready, especially in knowledge-intensive environments.