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| Section | Objectives |
|---|---|
| Topic 1: Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
| Topic 2: Prompt Engineering for Agents | - System prompts and constraints - Zero-shot and few-shot prompting |
| Topic 3: Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Topic 4: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Topic 5: Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Topic 6: UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Topic 7: Autopilot for Everyone | - Use cases and capabilities - AI-assisted automation building |
| Topic 8: Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Topic 9: Agentic AI Fundamentals | - Agentic automation concepts - AI agents vs rule-based automation |
| Topic 10: Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
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NEW QUESTION # 45
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: C
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 # 46
What configuration options are available for setting up Context Grounding in UiPath?
Answer: C
Explanation:
Bis correct - UiPath providesend-to-end configuration capabilitiesforContext Grounding, including:
* Creating indexesin Orchestrator
* Controlling accessviafolder-level permissions
* Selecting LLMsfrom theLLM Gateway
* Keeping indexesup to dateusing theUpdate Context Grounding Index activity This allows agents to accessreal-time enterprise context, reducing hallucinations and enhancing accuracy when performing actions or generating responses.
Option A underestimates the feature scope.
C and D are incorrect - UiPath supportsautomated syncs, granular access control, andmulti-model compatibility.
UiPath's platform treats grounding as agoverned, scalable enterprise feature, critical for AI safety and relevance.
NEW QUESTION # 47
Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?
Answer: A
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 # 48
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Answer: A
NEW QUESTION # 49
When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?
Answer: B
Explanation:
Cis correct -Clipboard AI, as embedded insideAutopilot for Everyone, is optimized forWindows environments, particularly when performingstructured copy-and-paste operations, such as extracting tables from a PDF and transferring them to Excel, Word, or web forms.
Best-use scenario:
* You copy structured data (like a table or text block)
* Paste it once into theAutopilot chat window
* Ask Autopilot to "paste this into [target app] in a structured format"
* It leverages Clipboard AI's logic to map and format the content intelligently Option A is incorrect - Autopilot doesn't queue multiple pastes. Each interaction is scoped.
B overstates platform independence - current support isWindows-first.
D is incorrect - Clipboard AI doesnot support macOS or cross-VM pastingyet.
This capability helpsnon-technical users automate repetitive copy-paste actions, improving speed, accuracy, and structure when transferring information across applications.
NEW QUESTION # 50
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