UiPath UiPath Certified Professional Agentic Automation Associate (UiAAA) UiPath-AAAv1 Exam Dumps: Updated Questions & Answers (February 2026)

Cis correct — mapping processes during agentic discovery is essential because it allows teams tozoom into specific tasks or sub-processeswhere agentic automation can deliver the highest value.

UiPath’sAgentic Design Blueprintmethodology emphasizes this as afoundational step. By creating detailed "as-is" process maps, teams can:

Spotrepetitive tasks(ideal for RPA)

Findjudgment-based decisions(ideal for agents)

Highlightescalation points, delays, and handoffs

This clarity helps identify:

Which actions can be automated

Which roles require agent augmentation

What context (data or documents) is needed

Option A skips process mapping and risks missing real value.

B is too high-level — real insights come from step-level granularity.

D is misleading — mapping is necessary butnot sufficientfor full implementation.

Accurate process mapping creates avisual and logical foundationfor designing agents that integrate seamlessly into workflows — targeting the right problems and unlocking measurable ROI.

The correct answer isC. In UiPath'sContext Groundingconfiguration, the“Number of results”setting directly affects how manychunks of indexed knowledgeare retrieved and passed to the LLM at runtime. These chunks come from preprocessed documents and are used to build thegrounding payload— the content added to the agent's prompt for context-aware generation.

By increasing the number of results:

The LLM has access tomore context, which can improve response quality if the added information is relevant.

However, it alsoincreases the token load, which can reduce prompt space or introduce irrelevant noise if poorly tuned.

Reducing the number of results leads tomore focused prompts, with only top-ranked relevant chunks (based oncosine similarity) included. This is crucial when using large indexes or when LLM context windows are limited.

Option A confuses this setting with similarity threshold tuning, which is a separate parameter.

Option B is false — the agent doesnot ignore contextunless context grounding is disabled.

Option D misrepresents the function — Orchestrator folder selection is unrelated to this retrieval setting.

In summary, the “Number of results” setting allows fine-tuning ofhow much supporting context is retrievedand passed to the model. It is a key control in optimizing performance, precision, and relevance of grounded agent responses.

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.

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.

Bis the correct answer — in UiPath's Agent Builder (Studio Web),descriptions for input and output arguments serve as grounding contextfor the agent. These descriptions help the LLMunderstand what each argument represents, how it should be used in the generation process, and how to structure its outputs.

This is especially critical for:

Inputs like {{CUSTOMER_ISSUE}} — the agent needs to know it’s a complaint, question, or error

Outputs like {{TROUBLESHOOTING_STEPS}} — the agent should format these as steps, not just a summary

These descriptions:

Improve theaccuracy of prompt generation

Ensure the agentreturns structured, expected data

Help guide LLM behavior in multi-step or dynamic workflows

Option A is incorrect — Orchestrator triggers donot auto-mapbased on descriptions.

C is false — descriptions donot make arguments mandatory.

D is incorrect —output arguments benefit greatly from descriptions, especially for guiding LLMs on return format and content.

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— UiPathMaestroenablesagentic orchestrationby serving as aprocess modeling and execution layerfor AI agents, RPA bots, human reviewers, and external systems. It supports BPMN-based modeling and integrates bothUiPath-built agentsandexternal agents, such as those fromLangChain,CrewAI, orAgentforce.

Maestro provides aconsistent frameworkthat allows:

InvokingLLM-powered agentsas subprocesses or service calls

Managingescalations and human-in-the-loop workflows

Defining structuredinputs, outputs, and triggersusing visual tools

Coordinating acrosshybrid environments, mixing RPA, agents, and APIs

This aligns with UiPath’sAgentic Automation vision, where agents are not isolated but operate withinenterprise-grade governance and control structures. Maestro enables scalable deployment ofgoal-driven, adaptive agentsinside complex, orchestrated processes.

Option A is incorrect — Maestro doesn’t embed code scripts or rely solely on external runtimes.

B is false — Maestro is broader than just Agentic and Integration tasks.

D is outdated — Maestro can orchestrate third-party agents with human review checkpoints via its own framework.

Maestro essentially acts as thecentral nervous systemfor agent coordination, making C the most accurate answer.

Cis correct — according to UiPath documentation,key-value pairs stored in Agent Memorypersist for12 months by default.

Agent Memoryis a persistent storage layer allowing agents to:

Recall decisions or context across runs

Store user preferences, status, or temporary flags

Maintain statefulness without relying on external databases

This capability is especially useful for:

Omnichannel customer interactions

Preference-aware recommendations

Tracking previously taken actions for continuity

Although memory storage is long-lasting (12 months), developers can:

Manually resetor expire entries

Use different memory scopes (e.g., per-user, per-agent)

Design memory-aware flows for personalization

Option D is incorrect — memory isnot auto-cleared on version updates.

A and B understate the retention policy — default expiration is clearly documented as12 monthsunless changed manually.

Agent Memory is a powerful enabler ofcontext-rich, stateful automations, especially for conversational or ongoing interactions.

Comprehensive and Detailed Explanation (from UiPath Agentic Automation documentation):

The correct approach isC, as it best reflects thefew-shot prompting pattern, which is a well-documented and recommended technique in both UiPath Autopilot™ and broader agentic AI design for improvingintent classificationaccuracy.

InUiPath Agentic Automation, especially inPrompt Engineering, few-shot examples serve to "ground" the Large Language Model (LLM) with task-specific context. Providingstructured input-output pairs(as shown in option C) allows the model to learn from the context and mirror the expected output more reliably — enhancing classification precision.

For instance, UiPath recommends using clearly formatted training examples in this structure:

Input: "[Text]"

Output: "[Label]"

This aligns with UiPath’s guidance under thePrompt Engineering Framework, which highlights that usingfew-shot exemplars with clear task demonstrationsignificantly improves model performance over zero-shot or ambiguous input formats (as in options A or B). Option D also underperforms due to insufficient grounding.

UiPath emphasizes the importance oflabel clarity,format consistency, andexplicit instruction— all of which are satisfied in Option C. This method also supportspromptgeneralizationfor new inputs by modeling how categorization should happen, not just what categories exist.

This technique is crucial in real-world agentic workflows where LLMs handle noisy, unstructured data (like emails), and are expected to trigger appropriate downstream actions such as ticket creation, escalation, or filtering.

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.