UiPath UiPath-AAAv1 - UiPath Certified Professional Agentic Automation Associate (UiAAA)

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UiPath UiPath-AAAv1 Exam Syllabus Topics:

UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:

1. When you want a connector field value to be inferred dynamically at run time, which input method should you select in the activity tool?

A) Argument
B) Clear value
C) Prompt
D) Static value

2. What is one of the key benefits of providing RAG as a service to UiPath generative AI experiences?

A) It exclusively provides access to historical data sources without supporting real-time updates.
B) It reduces the risk of hallucination by referencing ground truth data stores.
C) It directly increases the LLM context window size without any interaction with knowledge bases.
D) It eliminates the need for knowledge bases by integrating all proprietary data directly into generative applications.

3. 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"

4. Why is it important to include examples in prompts?

A) Including examples should only focus on edge cases while ignoring typical scenarios for better variety in results.
B) Carefully chosen examples help guide the agent and improve its ability to generalize across different scenarios.
C) Including examples guarantees output accuracy without any need for further adjustments or refinements.
D) Examples should be omitted to allow the AI to create responses entirely from general knowledge without guidance.

5. 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) Focus only on complaint categorization and rely on post-processing to handle emotional nuance.
B) Remove detailed task instructions to give the Agent more freedom in interpreting customer messages.
C) Include explicit context explaining the goal of sentiment analysis and define constraints for identifying urgency.
D) Provide vague constraints in an emotional tone.

Solutions: