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

1. 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 directly increases the LLM context window size without any interaction with knowledge bases.
C) It eliminates the need for knowledge bases by integrating all proprietary data directly into generative applications.
D) It reduces the risk of hallucination by referencing ground truth data stores.

2. Why is it essential to provide a focused description and usage guidance when adding a tool for an agent?

A) It limits the agent's actions to only those explicitly allowed by the user prompt, preventing incorrect reasoning.
B) It allows agents to execute all types of actions automatically, including Context Grounding and Escalations.
C) It ensures the agent understands the tool's purpose and can use it effectively in relevant scenarios.
D) It guarantees that agents can access and modify any business application data, even without tool integration.

3. An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
* Retrieve relevant troubleshooting steps from the web.
* Notify the user via Slack if a solution is found.
* Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?

A) Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than
30 for escalation processes.
B) Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.
C) Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.
D) Use random input sampling across all tools and rely on the default "LLM-as-a-Judge" assertion.

4. 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) Include three random emails and let the LLM guess the intent.
B) Show one example and leave the label blank for inference.
C) Use unlabeled prompts followed by ranked categories:
Classify this. "Need update on report." - [1] Urgent [2] Normal [3] Spam
D) Use examples such as:
Input: "Please address this issue immediately, server is down!" Output: "Urgent"

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) Declare the argument in the system prompt; any text surrounded by angle brackets (e.g.,
<CUSTOMER_EMAIL>) will be substituted automatically.
B) Create the argument in Data Manager and reference it verbatim inside double curly braces, e.g.,
{{CUSTOMER_EMAIL}}, so the name matches exactly.
C) Use single braces like {CUSTOMER_EMAIL}, because the platform automatically normalizes the identifier.
D) Simply mention the variable name in plain prose-the Agent will infer the value from the workflow without special syntax.

Solutions: