Free Practice Questions for UiPath UiPath-AAAv1 Exam

The correct answer isC— UiPath’sImpact and Feasibility Matrixis a structured tool used in thediscovery and prioritizationphase of agentic automation. It enables teams toevaluate and rank automation opportunitiesbased on two key dimensions:

Impact: Thebusiness valuedelivered — including time savings, risk reduction, efficiency, or user experience improvement.

Feasibility: Howpracticalorcost-effectiveit is to implement — considering technical complexity, data availability, resource constraints, and integration readiness.

This matrix helps classify use cases into quadrants such as:

Quick Wins(High Impact, High Feasibility)

Strategic Bets(High Impact, Low Feasibility)

Do Later(Low Impact, High Feasibility)

Avoid or Backlog(Low Impact, Low Feasibility)

UiPath emphasizes that this method ensures teams focus efforts whereagentic automation can create real business value quickly— avoiding wasted time on low-priority or hard-to-execute ideas.

Options A and B are partial approaches that ignore one of the two axes.

D is incorrect — not all processes should be automated, especially if they're low-value or high-risk.

This balanced framework is a core part of UiPath’sAgentic Design Blueprintmethodology for aligning automation with strategic priorities.

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.

The correct answer isD— selecting“Argument”allows a field value in an activity (such as a connector or tool call) to bedynamically inferred at runtime, based on variables, agent state, or previous node outputs.

UiPath Autopilot™ and Studio Web use the“Argument”option inactivity configurationto passdynamic values, especially in agentic workflows where:

Outputs of one step must inform inputs of the next

Contextual reasoning or prompt outputs need to feed tool parameters

Escalation decisions or classifications affect API calls or record updates

This is fundamental in making agent behavioradaptive and responsive to user context— a key trait of UiPath’s agentic orchestration layer.

Other options:

A (Static value) is hardcoded

B (Clear value) wipes any existing input

C (Prompt) is used when engaging the LLM, not connectors

Dis correct — this example follows thegold standard for few-shot prompting, as defined in UiPath’s Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:

Consistent structure to follow

Explicit tone classification

Variety across sentiment categories

Each example models the task exactly as it should be performed:

Input: [Text]

Output: [Label] (Positive, Neutral, Negative)

This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.

Option A (listing keywords) lacks structure and will not generalize well.

B is incomplete — there’s no output for the model to learn from.

C uses a rating scale, which doesn’t match the classification labels needed.

UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically — especially when working with ambiguous or emotionally nuanced language.

This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing — making the agent more reliable in real-world scenarios.

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.

Bis correct — to pass runtime values into an agent’s prompt in UiPath, you must:

Declare the variable inData Manager

Reference it inside theuser/system promptusingdouble curly braces, e.g., {{CUSTOMER_EMAIL}}

This ensures the platform can:

Substitute values at runtime

Maintain traceability between arguments and prompts

Provide context grounding for the LLM

Option A is incorrect — angle brackets are not used for substitution.

C is wrong — single braces {} are not valid for UiPath’s binding syntax.

D is unreliable — LLMs do not infer values from prose without structured substitution.

This technique ensures consistentparameter injectionfor context-aware agent behavior.

Cis correct —Clipboard AI, as embedded insideAutopilot for Everyone, is optimized forWindows environments, particularly when performingstructured copy-and-paste operations, such as extracting tables from a PDF and transferring them to Excel, Word, or web forms.

Best-use scenario:

You copy structured data (like a table or text block)

Paste it once into theAutopilot chat window

Ask Autopilot to “paste this into [target app] in a structured format”

It leverages Clipboard AI’s logic to map and format the content intelligently

Option A is incorrect — Autopilot doesn’t queue multiple pastes. Each interaction is scoped.

B overstates platform independence — current support isWindows-first.

D is incorrect — Clipboard AI doesnot support macOS or cross-VM pastingyet.

This capability helpsnon-technical users automate repetitive copy-paste actions, improving speed, accuracy, and structure when transferring information across applications.

Bis correct — the bpmn.uipath.com canvas is alightweight sandbox environmentfordrafting and visualizing agentic processes, butdoes not include full implementation capabilities. It is part of UiPath’s broaderMaestro experience, designed forearly-stage discovery, collaboration, and ideation.

Key characteristics:

Drag-and-dropBPMN modeling

Ability tooutline agents, decisions, automations, escalations

Useful forcollaborating with stakeholdersbefore technical development begins

Lacksdirect execution, tool integration, or runtime support

It is not a replacement forStudio WeborAutomation Cloud, which are used for:

Full implementation

Connecting to tools, prompts, or systems

Deployment and testing

Option A is incorrect — implementation requires transition intoStudio Web.

C is false — the tool is formodeling, not template import/export.

D misrepresents its role — it'snot the full-featured modeling tool, but adiscovery-phase sandbox.

Best practice: use bpmn.uipath.com todesign collaboratively, then export or map the flow inton8n,Studio, orMaestro production canvasfor build-out and testing.