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UiPath UiPath-AAAv1 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Orchestration & Human-in-the-Loop | 20% | - Service tasks and agent invocation - Escalations and Action Center - UiPath Maestro and BPMN modeling |
| Governance, Evaluation & Trust | 15% | - Agent performance evaluation - Observability and reliability - Guardrails and responsible AI |
| Agent Design & Development | 25% | - Tools, connections and integration services - Agent Builder in Studio Web - Prompt engineering best practices |
| Context & Knowledge Management | 20% | - Context packages and grounding - Knowledge base integration - Data sources and retrieval methods |
| Agentic Automation Fundamentals | 20% | - AI, LLM and generative AI concepts - Core principles of agentic automation - Agents vs traditional robots |
UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
Question 1
When configuring escalations for an agent, what is a key step to ensure the agent knows when to use the escalation during execution?
A. Utilize required fields in the inputs section of the escalation to define conditions for triggering escalations dynamically.
B. Configure escalation behavior entirely within the outcome behavior section, specifying how each resolution should be handled.
C. Add a prompt in the properties panel to help the agent determine the appropriate circumstances for using the escalation.
D. Directly assign an escalation recipient to ensure proper routing, which eliminates the need for agent- specific prompts in the escalation logic.
Question 2
A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?
A. The LLM's tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.
B. The inconsistency is related to the token limit defined for the prompt's length, which affects the LLM's ability to complete a response rather than its understanding of technical terms.
C. The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
D. The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
Question 3
When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?
A. When you plan to paste several different tables in succession during the same chat and expect Autopilot for Everyone to queue each paste automatically.
B. When you are using macOS and want Autopilot for Everyone to perform a copy and paste on a Linux VM.
C. Whenever you need to paste any content regardless of operating system, file type, or the number of pastes.
D. When you are working on a Windows machine and need to perform a single AI-powered paste of a table (for example, from a PDF) into another application directly from the chat interface.
Question 4
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?
A. Input: "The app is okay I guess." # Output:
B. Options: List words like: "great, okay, bad" and map them to tone.
C. Input: "I love the new design, very intuitive!" Output: "Positive"
Input: "Nothing special, just works." Output: "Neutral"
Input: "Terrible experience, won't use again." Output: "Negative"
D. "Text" Use a multiple-choice table with numerical ratings from 1-5.
Question 5
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?
A. Random sampling with A/B testing.
B. Model-graded evaluation to capture nuanced style and relevance.
C. Evaluation using a character count threshold to assess message quality.
D. Deterministic evaluation using a checklist of key phrases.
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: D | Question 4 Answer: C | Question 5 Answer: B |







