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UiPath UiPath-AAAv1 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Context Grounding (RAG) | - Data grounding strategies - Retrieval-Augmented Generation concepts |
| Topic 2: Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Topic 3: Agent Blueprint Design | - Workflow decomposition - Agent architecture design |
| Topic 4: Prompt Engineering for Agents | - Zero-shot and few-shot prompting - System prompts and constraints |
| Topic 5: Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Topic 6: Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Topic 7: UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Topic 8: Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Topic 9: Agentic Orchestration (Maestro) | - Workflow orchestration with agents - BPMN-based process design |
| Topic 10: Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
1. When creating an Action app, what is the purpose of defining the "Approve" and "Deny" outcomes within the Action schema?
A) To dynamically update user-facing form labels with the action result.
B) To guide the agent's next steps based on the review results of Input/Output properties.
C) To save user input as mandatory action schema properties during automation execution.
D) To ensure the app validates search results and prevents faulty submissions.
2. You are building an agent that classifies incoming emails into one of three categories: Urgent, Normal, or Spam. You want to improve accuracy by using few-shot examples in a structured format. Which approach best supports this goal?
A) Use unlabeled prompts followed by ranked categories:
Classify this. "Need update on report." - [1] Urgent [2] Normal [3] Spam
B) Show one example and leave the label blank for inference.
C) Include three random emails and let the LLM guess the intent.
D) Use examples such as:
Input: "Please address this issue immediately, server is down!" Output: "Urgent"
3. How long does a key-value pair stored in Agent Memory remain available before it expires by default?
A) Until the agent version is updated, after which key-value pairs are automatically cleared
B) 6 months
C) 3 months
D) 12 months
4. Why would you choose the Argument input method for an activity field?
A) Lets the agent infer the field value at runtime using the Description and its reasoning.
B) Prompts a person to supply the value each time the field is evaluated at runtime.
C) Applies one constant value you enter during design every time the agent executes the activity.
D) Receives a runtime value from an agent input argument defined earlier in the workflow.
5. 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) "Text" Use a multiple-choice table with numerical ratings from 1-5.
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) Input: "The app is okay I guess." # Output:
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: C |


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