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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 |
| Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
| UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
| Agent Blueprint Design | - Workflow decomposition - Agent architecture design |
| Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
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NEW QUESTION # 54
A team is building an AI agent that drafts personalized marketing emails. The quality of the drafts depends on tone, alignment with brand voice, and personalization. What evaluation approach is best?
Answer: A
Explanation:
Bis correct - for tasks involvingtone, style, brand alignment, and personalization,model-graded evaluationis the best choice.
UiPath'sagent evaluation frameworksupports multiple types of evaluation:
* Model-graded: LLMs score or classify outputs based on nuanced criteria (e.g., tone match, relevance)
* Human-graded: For subjective tasks
* Deterministic: For strict accuracy checks (e.g., regex, classification) In creative tasks likeemail drafting, deterministic methods (D) or length-based metrics (A)fail to capture nuance.
A/B testing (C) is useful in live experiments, but not for structured evaluation during development.
Model-graded evaluations enablescalable quality checksfor outputs that mustfeel human, on-brand, and context-aware- essential for personalized communication.
NEW QUESTION # 55
How does the impact and feasibility matrix assist in prioritizing agentic automation use cases?
Answer: B
Explanation:
The correct answer isC- UiPath'sImpact and Feasibility Matrixis a structured tool used in thediscovery and prioritizationphase of agentic automation. It enables teams toevaluate and rank automation opportunitiesbased on two key dimensions:
* Impact: Thebusiness valuedelivered - including time savings, risk reduction, efficiency, or user experience improvement.
* Feasibility: Howpracticalorcost-effectiveit is to implement - considering technical complexity, data availability, resource constraints, and integration readiness.
This matrix helps classify use cases into quadrants such as:
* Quick Wins(High Impact, High Feasibility)
* Strategic Bets(High Impact, Low Feasibility)
* Do Later(Low Impact, High Feasibility)
* Avoid or Backlog(Low Impact, Low Feasibility)
UiPath emphasizes that this method ensures teams focus efforts whereagentic automation can create real business value quickly- avoiding wasted time on low-priority or hard-to-execute ideas.
Options A and B are partial approaches that ignore one of the two axes.
D is incorrect - not all processes should be automated, especially if they're low-value or high-risk.
This balanced framework is a core part of UiPath'sAgentic Design Blueprintmethodology for aligning automation with strategic priorities.
NEW QUESTION # 56
When creating an Action app, what is the purpose of defining the "Approve" and "Deny" outcomes within the Action schema?
Answer: C
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 # 57
Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?
Answer: D
Explanation:
The correct answer isB- UiPath'sAgentic Blueprint Design processemphasizes the importance of grounding automation opportunities inreal user context and operational pain points.
During thevalidation stage, developers and stakeholders assess:
* Specific persona rolesand responsibilities
* Current pain pointsandtime-consuming tasks
* Impact potentialof agent assistance
This ensures the agent story reflectsvalue-driven automation, not just technical ambition. It also validates that the agent solves areal bottleneck- such as handling repetitive approvals, prioritizing requests, or managing context-based escalations.
UiPath warns against the pitfalls outlined in A, C, and D:
* A and D overlookpersona-centricity, which is essential.
* C skips the criticaldiscovery and mappingphase that should come before prototyping.
By focusing on B, teams ensure their agent ispurpose-builtfor the right users, delivering measurable outcomes aligned to business needs.
NEW QUESTION # 58
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Answer: A
NEW QUESTION # 59
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