Free UiPath UiPath-AAAv1 Practice Exam with Questions & Answers | Set: 2

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 is A — RAG (Retrieval-Augmented Generation) enhances generative AI experiences in UiPath by providing grounded, context-relevant data at runtime, which significantly reduces hallucinations.

Here’s how it works:

When an LLM receives a query, RAG pulls relevant documents or snippets from enterprise data sources (like knowledge bases, SharePoint, Confluence).

This content is passed to the LLM as context, enabling the model to respond using ground truth, not generic or fabricated knowledge.

UiPath’s GenAI platform and agentic agents use RAG to:

Enrich prompt context

Drive document-based answers

Support fact-checked decisions in customer service, HR, IT, etc.

Option B is false — RAG doesn’t alter the LLM’s context window.

C is incorrect — RAG works because it queries live knowledge bases.

D is wrong — RAG supports real-time dynamic data, not just historical.

Dis correct —goal-oriented executionis a core design principle in autonomous and agentic systems, including those built in UiPath's agent framework. It ensures that every decision, action, or tool invocation isaligned with a clearly defined outcome, such as resolving a ticket, completing a form, or drafting a report.

In UiPath’sagent design methodology, agents are given:

Adefined role(e.g., invoice reviewer, feedback classifier)

Agoal(e.g., triage input, approve/reject based on rules)

Constraints and context to operate within

This focus ensures agents don’t just act reactively — theypursue a target stateand adapt dynamically based on available information and decision rules.

Option A misunderstands autonomy — randomness undermines reliability.

B ignores the prioritization mechanism that’s critical for agents.

C confusesspeed with success— in goal-oriented systems, theright outcomeis more important than speed alone.

Goal alignment is what enables agents toreason, prioritize, and escalateintelligently — making autonomous execution not only possible but scalable and safe.

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.

Cis correct — agents in UiPath canintelligently automate complex procurement workflowsby combining tools likedocument extraction,web search for price benchmarks,policy validation, andLLM-based reasoning.

In this use case:

The agent extractsstructured data(item, price, quantity) from multiple quotations

Compares prices withexternal market sourcesusingWeb Searchor integrated APIs

Appliescompany policies or thresholdsusing system prompts and guardrails

Flags anomalies, escalates exceptions, or provides summarized comparisons

This reduces:

Manual effort

Human error

Turnaround time for approvals

And increases:

Policy compliance

Market alignment

Decision speed for procurement officers

Options A, B, and D all fall short of UiPath agent capabilities. These responses describepassive or limitedautomations, whereas agents are built to operateproactively and contextually, especially in high-value business functions like procurement.

This example reflects theagentic automation blueprintat work — combining perception, decision, and action across multiple systems in real time.

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 — for tasks involvingtone, style, brand alignment, and personalization,model-graded evaluationis the best choice.

UiPath’sagent evaluation frameworksupports multiple types of evaluation:

Model-graded: LLMs score or classify outputs based on nuanced criteria (e.g., tone match, relevance)

Human-graded: For subjective tasks

Deterministic: For strict accuracy checks (e.g., regex, classification)

In creative tasks likeemail drafting, deterministic methods (D) or length-based metrics (A)fail to capture nuance.

A/B testing (C) is useful in live experiments, but not for structured evaluation during development.

Model-graded evaluations enablescalable quality checksfor outputs that mustfeel human, on-brand, and context-aware— essential for personalized communication.

The correct answer isD, and this is a foundational concept in UiPath’sAgentic Discovery and Design Blueprint methodology.

Anagent storyserves as aclear, narrative-driven blueprintthat describes:

What the agent does

For whom it works

When it activates

How it makes decisions

What success looks like

UiPath emphasizes that a well-crafted agent story ensures alignment betweenbusiness stakeholders,subject matter experts (SMEs), andtechnical developers. It keeps the development team focused on value delivery by outlining thecore capabilities,contextual behavior, andinteractionsof the agent in a human-readable form.

This approach is critical during thedesign phase, as it:

Prevents scope creep

Clarifies success metrics

Enhances stakeholder buy-in

Anchors prompt design, orchestration, and escalation logic

UiPath also uses the agent story to guidegrounding strategies, tool selection, and even escalation paths — making it much more than a documentation artifact.

Options A, B, and C misrepresent the function of agent stories. Only D captures its value in focusing the team onwhat matters most for delivering real business outcomes.