UiPath UiPath-AAAv1 Dumps Questions Answers

Cis correct — an agentic story that falls into the"Do Later"quadrant typically representshigh feasibility but low impact.

In UiPath’sImpact vs. Feasibility Matrix, used during theAgentic Discoveryphase, automation ideas are evaluated on:

Feasibility(ease of implementation)

Impact(business value, time saved, ROI)

Quadrants:

Quick Wins: High impact, high feasibility

Do Later: Low impact, high feasibility

Strategic Bets: High impact, low feasibility

Avoid/Backlog: Low on both

‘Do Later’ agentic stories are often simple to automate but don’t deliver meaningful outcomes — e.g., automating low-volume tasks or internal reports with limited audience.

Focusing onimpactful use casesensures agent development time translates to real business value — one of the key lessons from UiPath’s agentic blueprint methodology.

Cis correct — LLMs like those used in UiPath’s Agentic Automation rely heavily ontokenization, which breaks input text into subword units (tokens). When complex technical terms (e.g., “UiPath.Orchestrator.API”) aresplit across multiple tokens, the model may not interpret themconsistently or accurately, especially if:

They're rare or domain-specific

Appear in different token contexts

Are inconsistently represented in training data

This is a common challenge in fine-tuning LLMs fortechnical documentation, where small changes in tokenization can shift meaning or relevance weighting. It’s why UiPath emphasizesprompt engineeringandcontext groundingto mitigate misinterpretation.

A is incorrect because thetoken limitaffects response length, not term understanding.

B is misleading — frequency matters, butsemantic relationshipsalso influence interpretation.

D is factually wrong — LLMs absolutely rely on tokenization and arenot rule-basedwith pre-programmed definitions.

Understanding how tokenization impacts prompt fidelity is critical when building agents that use LLMs to generatestep-by-step or technical outputs.

Bis correct — when a field (likeChannel Id) is set toPrompt, the agent will attempt to infer its valueat runtime, based on theinstructions in the promptand the context provided.

This is the default and preferred mode for agent tools when:

The agent has enough context or memory to decide

You wantLLM autonomyin filling the field dynamically

You're using prompt instructions like: “Post to the user’s default Slack channel”

Option A is incorrect — “Argument” is used when you're passing aspecific variableinto the agent prompt (not inferred).

C misunderstands data flow direction — “Output” is not relevant for input fields.

D is invalid — “Variable” is not the standard method for field inference in this scenario.

This aligns with UiPath’sagent + tools orchestrationmodel usingStudio Web’s low-code agent builder.