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| Section | Objectives |
|---|---|
| Topic 1: Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Topic 2: Prompt Engineering for Agents | - System prompts and constraints - Zero-shot and few-shot prompting |
| Topic 3: Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Topic 4: Agentic Orchestration (Maestro) | - Workflow orchestration with agents - BPMN-based process design |
| Topic 5: Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Topic 6: Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Topic 7: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Topic 8: UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Topic 9: Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Topic 10: Escalations & Human-in-the-Loop | - Action Center workflows - Exception handling and escalation patterns |
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NEW QUESTION # 10
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 # 11
What is a characteristic of using Business Process Model and Notation by process excellence practitioners?
Answer: B
Explanation:
The correct answer isC-Business Process Model and Notation (BPMN)is astandards-based modeling languageused byprocess excellence practitionersto visually define, communicate, and govern business workflows.
In UiPath'sMaestroorchestration platform, BPMN acts as acollaborative bridgebetween:
* Business stakeholders(who define processes and goals)
* Technical implementers(who build automations and agent logic)
BPMN includes rich constructs such as:
* Gateways for conditional logic
* Events for escalations and errors
* Tasks, subprocesses, and human interventions
This makes itideal for dynamic, agentic workflows- not just static process mapping.
A and B are false - BPMN is built foradaptive,automated, andcollaborativeorchestration.
D is wrong - BPMN supportserror handling, retries, and fallback flows, all critical in agentic automation.
NEW QUESTION # 12
What is a key feature of zero-shot prompting?
Answer: C
Explanation:
The correct answer isA- zero-shot prompting refers toasking an LLM to perform a task without providing any prior examples in the prompt. In UiPath Agentic Automation, this is considered the simplest form of task prompting and is often used when:
* The request isstraightforwardorfamiliar to the LLM
* There'sno need for detailed contextor task demonstration
* You want rapid generation without lengthy prompt design
UiPath distinguisheszero-shot,few-shot, andchain-of-thought promptingas part of itsPrompt Engineering Toolkit. While zero-shot is fast and scalable, it's not ideal fornuanced or ambiguous tasks, which often benefit fromfew-shot examplesor structured reasoning steps.
Option B is misleading - complex scenarios usuallyrequiremore grounding.
C contradicts the definition of zero-shot.
D confuses prompting withmodel fine-tuning, which is a separate concept.
Zero-shot works well for common, templated tasks (e.g., classifying "Is this urgent?") but is less reliable in dynamic, multi-intent agent behaviors.
NEW QUESTION # 13
What is the defining characteristic of few-shot prompting?
Answer: B
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 # 14
For what primary reason should you supply a description for every input and output argument in an agent?
Answer: C
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 # 15
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