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| Section | Objectives |
|---|---|
| Prompt Engineering for Agents | - Zero-shot and few-shot prompting - System prompts and constraints |
| Autopilot for Everyone | - Use cases and capabilities - AI-assisted automation building |
| Context Grounding (RAG) | - Data grounding strategies - Retrieval-Augmented Generation concepts |
| Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
| Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Agent Discovery & Process Assessment | - Process suitability for agentic automation - Identifying automation opportunities |
| Agentic Orchestration (Maestro) | - Workflow orchestration with agents - BPMN-based process design |
| Agent Blueprint Design | - Workflow decomposition - Agent architecture design |
| UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
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NEW QUESTION # 16
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 # 17
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 # 18
For what primary reason should you supply a description for every input and output argument in an agent?
Answer: D
Explanation:
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.
NEW QUESTION # 19
A team is designing an agent to convert plain text meeting notes into a formatted agenda (e.g., structured bullet points). Despite providing a few example transformations in the prompt, the agent generates agendas in inconsistent formats. What critical step was likely overlooked?
Answer: C
Explanation:
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.
NEW QUESTION # 20
What type of agents can be invoked using the 'Start and wait for external agent' feature in UiPath Maestro?
Answer: C
Explanation:
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.
NEW QUESTION # 21
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