UiPath-AAAv1 Questions and Answers

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.

The correct answer isA—clear, structured instructions about output formatare essential for consistent LLM responses, even when few-shot examples are present.

UiPath’sPrompt Engineering Frameworkemphasizes the use of:

Explicit formatting rules(e.g., “Use bullet points with bolded headers”)

Output instructions(e.g., “List items under appropriate sections: Decisions, Action Items, Next Steps”)

Consistent prompt structure: combining examples with a detailedtask instruction

Without such instructions, LLMs — even with examples — mayhallucinate structure, interpret outputs loosely, or switch formats between runs.

This is especially important inagentic automation, where the output feeds downstream steps (e.g., emails, documents, summaries). Inconsistent format breaks reliability and automation flow.

Option B (length constraints) helps refine but isn’t the root issue here.

C (randomized formatting) introduces inconsistency — not helpful.

D describes the issue but not themissing critical step, which is the instruction.

UiPath advises combining:

Few-shot examples+

Task description+

Format constraints→ for best results in structured content generation.

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.

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.

The correct answer isA— in UiPath’s Agent Builder (Studio Web), when you add atool(e.g., Integration Service activity, process, API call), it's essential to include aclear description and usage instructions. This description serves as aguide for the LLM, helping it understand:

What the tool does

When to use it

What input/output fields are relevant

Agents rely on this metadata todecidewhen and how to call the tool appropriately during execution. For example, if a tool is meant to send a Slack message, the description should say:

“Use this tool to notify the support team when a high-priority ticket is detected.”

Without a clear tool description, agents may:

Misuse tools (e.g., calling the wrong one)

Fail to act when they should

Deliver inconsistent results due to lack of grounding

Option B is incorrect — tool access doesn’t grant unrestricted data privileges.

C is too narrow — the prompt alone doesn’t restrict reasoning; the tool description plays a key role.

D is false — tool execution depends on agent reasoning and prompt logic, not automatic access.

Adding focused usage guidance ensuressafe, relevant, and accurate tool invocation, which is essential in agentic workflows that combine LLM flexibility with enterprise-grade precision.

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.

Dis correct — UiPathContext Groundingsupports querying unstructured and semi-structured data, includingCSV files, by embedding their content intosemantic representationssuch asJSON-formatted chunksduring indexing.

Here’s how this works for CSVs:

UiPathparses the tabular dataand maps each row or section into asemantically rich format(e.g., JSON)

These JSON-structured embeddings are then stored inECS Indexes(Enterprise Context Store)

When an LLM agent queries the index, it retrieves themost contextually relevant data, even across large datasets

This unlocks:

Smarter question answeringfrom tabular data

Cross-referencing multiple fieldsin a single query

Enhanced LLM understanding by transforming flat rows intorelational, structured prompts

Option A is misleading — LLMs rely onsemantic similarity, not SQL-like structured queries.

B is false — CSV is not auto-converted into XLSX.

C is incorrect — streaming is not yet supported; indexing is a prerequisite.

In short, UiPath enablessemantic grounding of structured datalike CSVs by reformatting them intoJSON-style embeddings, improving retrieval quality, summarization, and task-specific use cases.

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.

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.

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 — 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.

The correct answer isA— UiPath clearly differentiatesGenerative AIfromAgentic AIbased on function and scope:

Generative AIfocuses oncontent creation: generating emails, blog posts, social posts, or product descriptions using LLMs.

Agentic AIwraps this output withcontextual automation— it interprets, makes decisions, triggers actions, and interacts across systems.

In this marketing scenario:

Generative AI might write an email campaign or social caption.

Agentic AI would decidewhento send it, towhom, and based onwhich signals or workflows— possibly also adjusting the content based on campaign performance, customer segments, or behavior.

UiPath’s Agentic Automation model positions agentic AI as the"doer"— an orchestrator of dynamic workflows, not just a content engine. That’s why it underpins use cases like intelligent triage, escalation, or campaign coordination.

Options B, C, and D conflate or reverse these roles, which don’t align with UiPath’s design guidance.

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.