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| Section | Objectives |
|---|---|
| Topic 1: Prompt Engineering for Agents | - Zero-shot and few-shot prompting - System prompts and constraints |
| Topic 2: Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Topic 3: UiPath Platform Components | - Agent Builder and Orchestrator basics - Studio Web and Autopilot |
| Topic 4: Escalations & Human-in-the-Loop | - Action Center workflows - Exception handling and escalation patterns |
| Topic 5: Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
| Topic 6: Agentic Evaluations & Governance | - Guardrails and validation logic - LLM-as-a-judge metrics |
| Topic 7: Context Grounding (RAG) | - Data grounding strategies - Retrieval-Augmented Generation concepts |
| Topic 8: Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Topic 9: Agent Discovery & Process Assessment | - Process suitability for agentic automation - Identifying automation opportunities |
| Topic 10: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
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NEW QUESTION # 49
How does adjusting the "Number of results" setting affect the agent's use of context from indexes?
Answer: C
Explanation:
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 retrieved and passed to the model. It is a key control in optimizing performance, precision, and relevance of grounded agent responses.
NEW QUESTION # 50
What type of agents can be invoked using the 'Start and wait for external agent' feature in UiPath Maestro?
Answer: B
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 # 51
Why is an agent story important in the development life-cycle?
Answer: D
Explanation:
The correct answer isD, and this is a foundational concept in UiPath'sAgentic Discovery and Design Blueprint methodology.
Anagent storyserves as aclear, narrative-driven blueprintthat describes:
* What the agent does
* For whom it works
* When it activates
* How it makes decisions
* What success looks like
UiPath emphasizes that a well-crafted agent story ensures alignment betweenbusiness stakeholders,subject matter experts (SMEs), andtechnical developers. It keeps the development team focused on value delivery by outlining thecore capabilities,contextual behavior, andinteractionsof the agent in a human-readable form.
This approach is critical during thedesign phase, as it:
* Prevents scope creep
* Clarifies success metrics
* Enhances stakeholder buy-in
* Anchors prompt design, orchestration, and escalation logic
UiPath also uses the agent story to guidegrounding strategies, tool selection, and even escalation paths - making it much more than a documentation artifact.
Options A, B, and C misrepresent the function of agent stories. Only D captures its value in focusing the team onwhat matters most for delivering real business outcomes.
NEW QUESTION # 52
What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
Answer: C
Explanation:
Bis correct - creating a newevaluation setin UiPath involves a multi-step process designed to enable qualitative 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.
NEW QUESTION # 53
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: B
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 # 54
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