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| Section | Objectives |
|---|---|
| Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Prompt Engineering for Agents | - Zero-shot and few-shot prompting - System prompts and constraints |
| Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Escalations & Human-in-the-Loop | - Action Center workflows - Exception handling and escalation patterns |
| Agentic Evaluations & Governance | - Guardrails and validation logic - LLM-as-a-judge metrics |
| Agentic Orchestration (Maestro) | - Workflow orchestration with agents - BPMN-based process design |
| Agentic AI Fundamentals | - Agentic automation concepts - AI agents vs rule-based automation |
| Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
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NEW QUESTION # 21
An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as
'Positive', 'Neutral', or 'Negative'. Which few-shot design is most helpful?
Answer: D
Explanation:
Dis correct - this example follows thegold standard for few-shot prompting, as defined in UiPath's Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:
* Consistent structure to follow
* Explicit tone classification
* Variety across sentiment categories
Each example models the task exactly as it should be performed:
* Input: [Text]
* Output: [Label] (Positive, Neutral, Negative)
This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.
Option A (listing keywords) lacks structure and will not generalize well.
B is incomplete - there's no output for the model to learn from.
C uses a rating scale, which doesn't match the classification labels needed.
UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically - especially when working with ambiguous or emotionally nuanced language.
This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing - making the agent more reliable in real-world scenarios.
NEW QUESTION # 22
What is the primary recommendation for usinghttps://www.google.com/search?q=bpmn.uipath.comto access the Maestro modeling canvas?
Answer: B
Explanation:
Bis correct - the bpmn.uipath.com canvas is alightweight sandbox environmentfordrafting and visualizing agentic processes, butdoes not include full implementation capabilities. It is part of UiPath's broaderMaestro experience, designed forearly-stage discovery, collaboration, and ideation.
Key characteristics:
* Drag-and-dropBPMN modeling
* Ability tooutline agents, decisions, automations, escalations
* Useful forcollaborating with stakeholdersbefore technical development begins
* Lacksdirect execution, tool integration, or runtime support
It is not a replacement forStudio WeborAutomation Cloud, which are used for:
* Full implementation
* Connecting to tools, prompts, or systems
* Deployment and testing
Option A is incorrect - implementation requires transition intoStudio Web.
C is false - the tool is formodeling, not template import/export.
D misrepresents its role - it'snot the full-featured modeling tool, but adiscovery-phase sandbox.
Best practice: use bpmn.uipath.com todesign collaboratively, then export or map the flow inton8n,Studio, or Maestro production canvasfor build-out and testing.
NEW QUESTION # 23
What is the defining characteristic of few-shot prompting?
Answer: A
Explanation:
Dis correct - the defining feature offew-shot promptingis the inclusion ofmultiple input-output examples within the prompt todemonstrate the desired behavior or output structureto the LLM.
In UiPath's Agentic Prompting practices, few-shot examples help:
* Anchor the model to a consistent format
* Reduce ambiguity in task instructions
* Improve performance in tasks like classification, transformation, or content generation Example:
Input: "My password isn't working."
Output: "Category: Login Issue"
Input: "App won't open."
Output: "Category: Access Error"
This trains the model within the prompt - no fine-tuning required - making it apowerful design patternin building intelligent agents.
Option A describeschain-of-thought prompting.
B refers tozero-shot prompting.
C refers toprompt chaining, used in advanced orchestration, not few-shot logic.
NEW QUESTION # 24
When creating an Action app, what is the purpose of defining the "Approve" and "Deny" outcomes within the Action schema?
Answer: B
Explanation:
The correct answer isB- defining outcomes like"Approve"and"Deny"within an Action schema is critical for guiding downstream logic in agent behavior, especially in scenarios involvinghuman-in-the-loop reviews.
According to UiPath's documentation forAction Center, outcomes act asexplicit decision points. When a user completes a review (e.g., a document, output, or classification), the selected outcome drives what the agent or automation should do next - for example:
* "Approve"might trigger further processing or submission.
* "Deny"could lead to rework, escalation, or termination of the process.
This is especially relevant inagentic workflows, where the agent offloads uncertain tasks to humans, and the human response informs the next step via outcome-driven branching logic.
Options A, C, and D refer to unrelated features like data validation, mandatory fields, or UI tweaks - none of which define thelogical consequencesthat outcomes control.
NEW QUESTION # 25
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: B
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 # 26
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