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| Section | Objectives |
|---|---|
| Topic 1: Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Topic 2: Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Topic 3: Autopilot for Everyone | - Use cases and capabilities - AI-assisted automation building |
| Topic 4: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Topic 5: Agent Discovery & Process Assessment | - Process suitability for agentic automation - Identifying automation opportunities |
| Topic 6: Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
| Topic 7: UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Topic 8: Agentic Evaluations & Governance | - Guardrails and validation logic - LLM-as-a-judge metrics |
| Topic 9: Prompt Engineering for Agents | - System prompts and constraints - Zero-shot and few-shot prompting |
| Topic 10: Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
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NEW QUESTION # 11
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: C
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 # 12
Why is it important to include examples in prompts?
Answer: C
Explanation:
Dis correct - includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.
UiPath encourages the use of examples for:
* Classification(e.g., labeling sentiment, email categories)
* Transformation tasks(e.g., turning unstructured text into tables)
* Step-by-step instructions(e.g., troubleshooting flows)
These examples serve two purposes:
* Pattern induction: The model picks up on consistent structures or rules used across examples.
* Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.
Best practice:
* Usetypical, real-world examplesrepresentative of the data the agent will encounter.
* Keep formatsclear and consistentacross input-output pairs.
* Pair examples withexplicit instructionsin the system or user prompt.
Option A is flawed - focusing only on edge cases can confuse the model.
B is false - omitting examples forces the LLM to guess the structure, reducing accuracy.
C is misleading - examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.
In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.
NEW QUESTION # 13
Which of the following best describes how agents handle dynamic environments?
Answer: C
Explanation:
Bis correct - one of the defining strengths ofUiPath's agentic automationis the ability for agents toadapt to dynamic environmentsusingLLMs and contextual grounding.
Agents differ from traditional RPA bots in that they:
* Interpret natural language
* Reason across structured and unstructured data
* Adjust outputs based onreal-time context, grounding, and updated knowledge When processes change - such as updates to escalation rules, variations in incoming requests, or new product names - agents can adjust without reprogramming, thanks to:
* Flexible prompts
* Grounded context from indexes or memory
* Few-shot or zero-shot inference capabilities
This adaptability makes agents ideal for scenarios likeemail triage,customer service, orknowledge work, where inputs and conditions vary.
Option A and D falsely suggest agents are rigid or fully dependent on human intervention.
Option C applies to classic RPA bots - not LLM-powered agents.
While agents don't"learn"in the ML retraining sense during execution, theydynamically interpret and adapt within the context of each session - a key feature enabled by UiPath's Autopilot™, Context Grounding, and agent memory frameworks.
This flexibility is foundational to deploying agents in environments whererules evolve, data flows shift, or human-like understanding is needed.
NEW QUESTION # 14
Which of the following best describes a challenge faced by traditional automation in complex business processes?
Answer: C
Explanation:
The correct answer isC, which highlights one of the core limitations of traditional rule-based automation (RPA) - itsinability to handle unstructured tasks that require human-like reasoning and contextual awareness.
According to UiPath's Agentic Automation documentation, traditional automation excels atrepetitive, rules- based, structuredtasks. However, it struggles when:
* Input data isunstructured(like emails, PDFs, or chat logs)
* Tasks requirecontextual understanding, decision-making, or judgment
* Processes span across systems with unpredictable flows (e.g., CRM + ERP + email) This is exactly whereAgentic Automationsteps in. It augments classic automation by embeddingLLMs, AI agents, and decision intelligenceto manage tasks involving ambiguity, variability, and natural language - things traditional bots cannot handle well.
Options A, B, and D are incorrect or misleading:
* A is false because traditional automation isnotflexible across varied workflows.
* B is theoppositeof traditional automation - it's agentic.
* D is inaccurate because RPA handles repetitive, structured tasks very well - that's its strength.
By addressing C, UiPath bridges the gap between deterministic automation and intelligent, adaptive systems that can trulyscale across complex, real-world business scenarios.
NEW QUESTION # 15
What is the significance of the "as-is" process map in identifying agentic automation opportunities?
Answer: B
Explanation:
The correct answer isA- the"as-is" process mapis a foundational tool in UiPath'sAgentic Blueprint Design framework. It visualizes thecurrent stateof how work is performed, enabling teams to:
* Identifymanual, repetitive, or time-consuming tasks
* Spotdelays, rework loops, or human bottlenecks
* Discover areas whereagents can augment or replace tasks
It acts as abaseline diagnostic, not an implementation-ready flow.
This is a key step in workshops led byprocess owners or business SMEs, before designing the "to-be" process where agents are introduced.
B is incorrect - the as-is map isnot final.
C refers to the optimized state, not the current one.
D confuses the mapping's role - it captures thepresent, not thegoals.
NEW QUESTION # 16
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