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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context & Knowledge Management | 20% | - Data sources and retrieval methods - Knowledge base integration - Context packages and grounding |
| Topic 2: Agent Design & Development | 25% | - Prompt engineering best practices - Tools, connections and integration services - Agent Builder in Studio Web |
| Topic 3: Governance, Evaluation & Trust | 15% | - Observability and reliability - Agent performance evaluation - Guardrails and responsible AI |
| Topic 4: Agentic Automation Fundamentals | 20% | - Agents vs traditional robots - AI, LLM and generative AI concepts - Core principles of agentic automation |
| Topic 5: Orchestration & Human-in-the-Loop | 20% | - Service tasks and agent invocation - Escalations and Action Center - UiPath Maestro and BPMN modeling |
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NEW QUESTION # 31
You want your agent to call an existing UiPath process by adding it in the Tools # Processes. Which prerequisite must be met before the process becomes selectable?
Answer: A
Explanation:
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.
NEW QUESTION # 32
An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
* Retrieve relevant troubleshooting steps from the web.
* Notify the user via Slack if a solution is found.
* Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?
Answer: D
Explanation:
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.
NEW QUESTION # 33
An agent is being designed to generate step-by-step troubleshooting guides for software issues. Testing shows that the guides lack clarity and include redundant steps, confusing users. What is the best refinement for the prompt?
Answer: A
Explanation:
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 includes clear 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.
NEW QUESTION # 34
A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?
Answer: A
Explanation:
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 engineeringand context 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.
NEW QUESTION # 35
What is a System Prompt?
Answer: C
Explanation:
Cis the correct answer - in UiPath's Agentic Automation framework, theSystem Promptis acrucial configuration elementthat defines theagent's identity, objectives, behavioral rules, and tool usage logic.
It typically includes:
* Agent Role: e.g., "You are a procurement assistant"
* Goals: "Classify, summarize, or validate supplier quotes"
* Constraints: e.g., "Don't exceed 100 words", "Only use escalation when criteria X is met"
* Tool Usage: "Use Slack tool to notify team if X occurs"
* Escalation Logic: "Escalate to human if confidence is below threshold"
* Context Integration: "Use grounded context from ECS Index when available" This helps the LLM behaveconsistentlyandtransparently, even in unpredictable or complex workflows. It also acts as thestarting configurationfor the agent - informing every decision it makes during runtime.
Option A is incorrect - System Prompts are written innatural language, not code.
B is false - they allow fordynamic adaptation, especially when used with memory and tools.
D is incomplete - the system promptdoes covergoals, constraints, and sequencing of steps.
Bottom line: theSystem Prompt is the "brain" behind the agent, telling it what to do, how to do it, when to act, and when to escalate - all in anatural language-driven, declarative format.
NEW QUESTION # 36
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