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UiPath UiPath-AAAv1 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Topic 2: Context Grounding (RAG) | - Data grounding strategies - Retrieval-Augmented Generation concepts |
| Topic 3: Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Topic 4: UiPath Platform Components | - Agent Builder and Orchestrator basics - Studio Web and Autopilot |
| Topic 5: Prompt Engineering for Agents | - Zero-shot and few-shot prompting - System prompts and constraints |
| Topic 6: Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Topic 7: Agentic Orchestration (Maestro) | - Workflow orchestration with agents - BPMN-based process design |
| Topic 8: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Topic 9: Agentic AI Fundamentals | - Agentic automation concepts - AI agents vs rule-based automation |
| Topic 10: Escalations & Human-in-the-Loop | - Action Center workflows - Exception handling and escalation patterns |
UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
1. When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?
A) Automatic conversion of CSV data into native XLSX files for enhanced compatibility.
B) Streaming support for real-time ingestion and CSV query execution without indexing.
C) Embedding data from CSV files into JSON templates for improved semantic similarity.
D) Support for structured queries tailored specifically for CSV data.
2. A company is integrating an Agent into its customer support workflow to detect sentiment and classify complaints (e.g., "Billing issue", "Product defect"). However, the Agent's responses often miss subtle emotional cues like frustration or urgency. What change to the prompt design would most improve the quality of sentiment detection?
A) Provide vague constraints in an emotional tone.
B) Include explicit context explaining the goal of sentiment analysis and define constraints for identifying urgency.
C) Remove detailed task instructions to give the Agent more freedom in interpreting customer messages.
D) Focus only on complaint categorization and rely on post-processing to handle emotional nuance.
3. When exploring agentic automation discovery, which dimension ensures the solution aligns with the responsibilities and challenges of the individuals involved?
A) Assessing structured and unstructured knowledge contexts required for the tasks but excluding the personas performing these operations.
B) Mapping systems, applications, and tools without understanding how they interact with human roles.
C) Defining the role or persona by considering the people performing the tasks and their needs, challenges, and responsibilities.
D) Focusing solely on task dependencies while neglecting the daily pain points of individuals executing these tasks.
4. When mapping business process steps to agent tasks using Task Capture, which BPMN element is mapped as a 'Decision' rather than as a unique element?
A) Exclusive Gateway
B) Task
C) User Task
D) Swimlane
5. A developer is implementing a few-shot structured prompt for an email classification task. The prompt includes examples of email subjects labeled with their respective classifications, such as "Spam" or "Work." What is the most important aspect to consider when selecting examples for the prompt?
A) Always use more than 10 examples, regardless of task complexity.
B) Use random and unrelated examples to test the prompt's robustness.
C) Include examples with intentionally incorrect labels to improve training.
D) Choose examples that are diverse, relevant, and typical of the task's expected input.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |



