Free Practice Questions for UiPath UiPath-AAAv1 Exam

Bis correct — creating a newevaluation setin UiPath involves a multi-step process designed to enablequalitative and quantitative review of agent behavior.

Steps include:

Namingthe evaluation set

Addinginput promptsandexpected outputs

Saving each test item (often called "evaluations")

Assigning evaluators, who will manually or automatically score the results

This process enablestestable, repeatable evaluationof agent behavior before deployment — ensuring the model produces correct, useful, and safe outputs.

Options A and C are incorrect:

A reverses the order: inputs and expected outputs are neededbeforeevaluators.

C is false — evaluation setscan be built from scratch.D implies scoring is automatic, but human reviewers or comparison logic are often required for nuanced evaluations.

This aligns with UiPath's best practices inagent validationand post-deployment assurance.

Dis correct — inTask CaptureandBPMN modeling, theExclusive Gatewayis the BPMN element that represents adecision point. It is used to:

Split the process intomutually exclusive paths

Route flow based on acondition or choice

When mapping these processes toagent behavior, the Exclusive Gateway typically translates to a“Decision” node, where the agent:

Evaluates logic (e.g., “Is amount > $10,000?”)

Selects one path forward (e.g., “Escalate” vs. “Auto-approve”)

This is a fundamental construct in UiPath’sagentic process modeling, enabling agents to handlebranching logic, make contextual choices, or call different tools based on runtime data.

Option A (Task) represents an activity, not a decision.

B (Swimlane) is used to group actions by role — not functional logic.

C (User Task) represents human involvement — not branching conditions.

UiPath emphasizes decision modeling to make agentsadaptive and responsive, and Exclusive Gateways are the tool to model such decisions cleanly and visually.

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.

The correct answer isC—Business Process Model and Notation (BPMN)is astandards-based modeling languageused byprocess excellence practitionersto visually define, communicate, and govern business workflows.

In UiPath’sMaestroorchestration platform, BPMN acts as acollaborative bridgebetween:

Business stakeholders(who define processes and goals)

Technical implementers(who build automations and agent logic)

BPMN includes rich constructs such as:

Gateways for conditional logic

Events for escalations and errors

Tasks, subprocesses, and human interventions

This makes itideal for dynamic, agentic workflows— not just static process mapping.

A and B are false — BPMN is built foradaptive,automated, andcollaborativeorchestration.

D is wrong — BPMN supportserror handling, retries, and fallback flows, all critical in agentic automation.

Ais correct — in UiPath’s agent framework, asystem promptserves as the agent’score grounding mechanism. It is responsible for:

Defining the agent’s identity(“You are an IT support assistant...”)

Outlining its goal(“Your job is to classify, triage, and resolve tickets...”)

Setting operational boundaries and behaviors

Specifying when to escalate to a humanor use tools

This aligns with UiPath’sContext Grounding strategy, which separatessystem prompts,user prompts, andtools orchestration. The system prompt providespersistent context, guiding the LLM's behavior consistently across user interactions and actions.

Option B downplays its influence — which is critical.

C reduces it to output formatting, which is only a small part.

D is unrealistic — LLMs generalize; they don’t require enumerating every path.

Correct system prompting ensuressafe, consistent, goal-aligned behaviorfrom the agent across dynamic scenarios.

Ais correct — improving sentiment detection in agents begins with awell-structured promptthat includesexplicit task contextand clearly defined expectations, especially when detecting nuanced emotions likefrustration, urgency, or sarcasm.

According to UiPath’sPrompt Engineering Framework, a strong prompt should include:

Atask objective: e.g., “Detect sentiment and urgency in user messages”

Definitions or rules: e.g., “Urgency includes time sensitivity, threats of cancellation, or escalated language”

Output constraints: e.g., “Classify as Positive, Neutral, Negative, and Urgent (Yes/No)”

This helps the LLM:

Anchor its reasoning to what urgency means inyour business context

Avoid hallucinations or misinterpretation of neutral phrases

Generateconsistently labeled outputsfor downstream automation or review

Option B lacks structure — emotional tone ≠ clarity.

C is risky — too much freedom leads to inconsistent results.

D separates tasks that arebest handled together, especially since emotion often influences how a complaint should be triaged.

Byembedding sentiment-specific logic into the prompt, UiPath agents become better equipped todetect critical issues in real time, enabling faster response and better customer experience.

This is a repeat of Question 16, and the correct answer remains A.

Even when few-shot examples are included, omitting clear formatting instructions leads to inconsistent outputs, which can break downstream processes in agentic automation.

UiPath’s Prompt Engineering guidance emphasizes that instruction clarity is as important as examples — especially when output format matters (like agendas, classifications, or structured text).

An optimal prompt includes:

A task description (e.g., “Convert meeting notes into a 3-section agenda”)

Clear format instructions (e.g., use bullet points, bold headers)

Few-shot examples

Optional constraints like length or tone

Without that first element — clear instructions — the LLM has to guess the output format, leading to variance and unreliability.

The correct answer isB— defining outcomes like“Approve”and“Deny”within an Action schema is critical forguiding downstream logic in agent behavior, especially in scenarios involvinghuman-in-the-loop reviews.

According to UiPath's documentation forAction Center, outcomes act asexplicit decision points. When a user completes a review (e.g., a document, output, or classification), the selected outcome drives what the agent or automation should do next — for example:

“Approve”might trigger further processing or submission.

“Deny”could lead to rework, escalation, or termination of the process.

This is especially relevant inagentic workflows, where the agent offloads uncertain tasks to humans, and the human response informs the next step via outcome-driven branching logic.

Options A, C, and D refer to unrelated features like data validation, mandatory fields, or UI tweaks — none of which define thelogical consequencesthat outcomes control.

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.