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UiPath UiPath-AAAv1 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Autopilot for Everyone | - Use cases and capabilities - AI-assisted automation building |
| Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
| Prompt Engineering for Agents | - System prompts and constraints - Zero-shot and few-shot prompting |
| Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Agent Discovery & Process Assessment | - Process suitability for agentic automation - Identifying automation opportunities |
| UiPath Platform Components | - Studio Web and Autopilot - Agent Builder and Orchestrator basics |
| Agentic Evaluations & Governance | - LLM-as-a-judge metrics - Guardrails and validation logic |
| Agent Blueprint Design | - Workflow decomposition - Agent architecture design |
| Context Grounding (RAG) | - Retrieval-Augmented Generation concepts - Data grounding strategies |
| Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
Question 1
For what primary reason should you supply a description for every input and output argument in an agent?
A. Argument descriptions are required only for input arguments; output arguments are inherently self- explanatory and do not benefit from them.
B. Clear descriptions help the agent understand how to use each argument effectively while generating or returning results.
C. Descriptions cause Orchestrator triggers to pre-populate the arguments automatically, eliminating manual mapping.
D. Adding descriptions forces Studio Web to treat all arguments as mandatory fields that block deployment if left empty.
Question 2
What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
A. Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.
B. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
C. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
D. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
Question 3
A company launches a marketing campaign powered by generative AI and agentic AI technologies. What best describes how their roles differ?
A. Generative AI focuses on improving customer sentiment analysis, while agentic AI refines campaign content.
B. Both generative AI and agentic AI collaborate to create promotional materials.
C. Generative AI produces outputs like text or images, while agentic AI focuses on intelligent automation, decision-making, and dynamic task execution.
D. Agentic AI designs campaign materials, while generative AI handles customer queries in real-time.
Question 4
A business is looking to automate its workflows and has both structured, repetitive tasks (like data entry) and unstructured, exception-heavy processes (such as responding to diverse customer queries). How should they combine agents and robots (RPA) to achieve optimal automation results?
A. Use agents for the structured, repetitive tasks, as they can follow deterministic rules efficiently while robots (RPA) handle unstructured workflows requiring adaptability, decision-making capabilities and contextual awareness.
B. Use agents exclusively, as they can cover both structured workflows and dynamic environments due to their probabilistic and adaptive nature.
C. Use robots (RPA) exclusively, as they are capable of adapting to dynamic workflows with exception handling and learning capabilities.
D. Use robots (RPA) for the structured, repetitive tasks, leveraging their rule-based approach for reliability and precision, while agents handle the unstructured processes by using their adaptive decision-making capabilities.
Question 5
When passing runtime data into an Agent, which approach ensures the input argument is actually available inside the user prompt at execution time?
A. Use single braces like {CUSTOMER_EMAIL}, because the platform automatically normalizes the identifier.
B. Simply mention the variable name in plain prose-the Agent will infer the value from the workflow without special syntax.
C. Create the argument in Data Manager and reference it verbatim inside double curly braces, e.g.,
{{CUSTOMER_EMAIL}}, so the name matches exactly.
D. Declare the argument in the system prompt; any text surrounded by angle brackets (e.g.,
<CUSTOMER_EMAIL>) will be substituted automatically.
Solutions:
| Question 1 Answer: B | Question 2 Answer: A | Question 3 Answer: C | Question 4 Answer: D | Question 5 Answer: C |
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