Last Attempt UiPath-AAAv1 Questions

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.

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.

Cis the correct answer — apersona-centered approachis a cornerstone of UiPath’sAgentic Discovery and Blueprint Designmethodology.

When identifying automation opportunities, UiPath stresses:

Understanding the actual people behind the process

Mapping theirpain points,repetitive tasks,decision fatigue, andworkflow bottlenecks

Designing agents thatserve that roleand embed naturally into their day-to-day responsibilities

This ensures agents are:

Valuable(they solve the right problems)

Adoptable(they fit into how people actually work)

Sustainable(they evolve with user needs)

Options A, B, and D areanti-patterns— each represents a discovery flaw where automation is misaligned due toignoring human context.

Persona definition is essential for designing agents thatact as reliable digital coworkers, not just process bots.

Cis correct — UiPath recommends structuringagent evaluationsaroundfunctional setsthat align with expected behavior and edge conditions. This strategy:

Validatesend-to-end logic, not just isolated tool usage

Helps assess whethertool combinationswork as designed

Supportstraceable diagnosisof failures or regressions

In this scenario:

Set 1: Valid Web Search results➝Slack notification (success path)

Set 2: Failed/irrelevant Web Search➝Escalation (fallback path)

Set 3: Edge cases (e.g., ambiguous input, multiple valid matches)

This avoids theredundancyandvolume bloatseen in options B and D.

Option A is too loose — relying solely on random inputs and "LLM-as-a-Judge" introduces risk ofincomplete testing.

Grouping byreal-world interaction patternsmirrors how agents behave in production. It ensures high coverage while keeping evaluation efficient, consistent, andtightly aligned with business logic.