UiPath-AAAv1 UiPath Certified Professional Agentic Automation Associate (UiAAA) Questions and Answers

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.

Dis correct — includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.

UiPath encourages the use of examples for:

Classification(e.g., labeling sentiment, email categories)

Transformation tasks(e.g., turning unstructured text into tables)

Step-by-step instructions(e.g., troubleshooting flows)

These examples serve two purposes:

Pattern induction: The model picks up on consistent structures or rules used across examples.

Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.

Best practice:

Usetypical, real-world examplesrepresentative of the data the agent will encounter.

Keep formatsclear and consistentacross input-output pairs.

Pair examples withexplicit instructionsin the system or user prompt.

Option A is flawed — focusing only on edge cases can confuse the model.

B is false — omitting examples forces the LLM to guess the structure, reducing accuracy.

C is misleading — examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.

In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.

The correct answer isB— UiPath’sAgentic Blueprint Design processemphasizes the importance of grounding automation opportunities inreal user context and operational pain points.

During thevalidation stage, developers and stakeholders assess:

Specific persona rolesand responsibilities

Current pain pointsandtime-consuming tasks

Impact potentialof agent assistance

This ensures the agent story reflectsvalue-driven automation, not just technical ambition. It also validates that the agent solves areal bottleneck— such as handling repetitive approvals, prioritizing requests, or managing context-based escalations.

UiPath warns against the pitfalls outlined in A, C, and D:

A and D overlookpersona-centricity, which is essential.

C skips the criticaldiscovery and mappingphase that should come before prototyping.

By focusing on B, teams ensure their agent ispurpose-builtfor the right users, delivering measurable outcomes aligned to business needs.

The correct answer isC, which highlights one of the core limitations of traditional rule-based automation (RPA) — itsinability to handle unstructured tasks that require human-like reasoning and contextual awareness.

According to UiPath’s Agentic Automation documentation, traditional automation excels atrepetitive, rules-based, structuredtasks. However, it struggles when:

Input data isunstructured(like emails, PDFs, or chat logs)

Tasks requirecontextual understanding, decision-making, or judgment

Processes span across systems with unpredictable flows (e.g., CRM + ERP + email)

This is exactly whereAgentic Automationsteps in. It augments classic automation by embeddingLLMs, AI agents, and decision intelligenceto manage tasks involving ambiguity, variability, and natural language — things traditional bots cannot handle well.

Options A, B, and D are incorrect or misleading:

A is false because traditional automation isnotflexible across varied workflows.

B is theoppositeof traditional automation — it's agentic.

D is inaccurate because RPA handles repetitive, structured tasks very well — that’s its strength.

By addressing C, UiPath bridges the gap between deterministic automation and intelligent, adaptive systems that can trulyscale across complex, real-world business scenarios.

Cis correct — the best refinement is toexplicitly instruct the agent to produce actionable, concise, and non-redundant steps. UiPath emphasizes that LLM outputs improve significantly when the prompt includesclear task goals + structure + tone guidelines.

In this case:

“Avoid repeating steps”

“Make each step actionable”

“Keep it short and clear”

…are examples ofinstructions that directly reduce confusion and redundancyin generated content.

Options A and B introduce vagueness or verbosity, which worsen the problem.

D removes detail — the opposite of what’s needed forstep-by-step clarity.

UiPath’s Prompt Engineering Toolkit recommendstight formatting, tone, and output constraintsfor high-quality, consistent automation guides.

Cis correct — useWeb Searchin an agent workflow when you need the LLM toquery public internet sources(e.g., news, pricing, documentation), butdon’t have a specific URL.

UiPath Autopilot and Agentic Agents distinguish:

Web Search: For open-ended discovery from the web (e.g., "find latest refund policies from airlines")

Web Reader: For extracting or summarizing content from aspecific, known URLor internal portal

Web Search is ideal for:

Aggregating public info

Real-time summaries

Context retrieval for grounding the prompt

A and B involveinternal sources— use tools likeKnowledge RetrievalorAPI connectorsinstead.

D calls fortargeted extraction, better suited toWeb Readerwith structured parsing.

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.

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.

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

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.

Dis correct — the defining feature offew-shot promptingis the inclusion ofmultiple input-output exampleswithin the prompt todemonstrate the desired behavior or output structureto the LLM.

In UiPath’s Agentic Prompting practices, few-shot examples help:

Anchor the model to a consistent format

Reduce ambiguity in task instructions

Improve performance in tasks like classification, transformation, or content generation

Example:

Input: “My password isn’t working.”

Output: “Category: Login Issue”

Input: “App won’t open.”

Output: “Category: Access Error”

This trains the model within the prompt — no fine-tuning required — making it apowerful design patternin building intelligent agents.

Option A describeschain-of-thought prompting.

B refers tozero-shot prompting.

C refers toprompt chaining, used in advanced orchestration, not few-shot logic.

Bis the correct answer — in UiPath’sAgent Builder (Studio Web), when you want to invoke an existing UiPath process from an agent (viaTools → Processes), that process must meettwo key prerequisites:

It must be published and deployed to a shared Orchestrator folder

You — and the agent — must have access to that folder

This ensures that:

The agent canlocate and run the processat execution time

Role-based access control (RBAC) is respected

Input/output arguments, execution logs, and exceptions are properly managed within the correct environment

This aligns with UiPath’sOrchestrator-integrated agent orchestration model, where security and deployment visibility are tightly governed. It also allows agent authors toreuse existing RPA logicinside dynamic agent flows without duplicating automation work.

Option A and D incorrectly imply that argument types affect process visibility — that’s false. Agents can invoke processes withany argument signature, as long as mapping is defined.

Option C is incorrect — publishing alone is not enough.Deployment and permissionsare required for the process to appear in the tool selector.

This model ensures that agents can call any compliant UiPath processsecurely, reliably, and in line with enterprise governance.

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.

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.