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

1. 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) Choose examples that are diverse, relevant, and typical of the task's expected input.
B) Use random and unrelated examples to test the prompt's robustness.
C) Always use more than 10 examples, regardless of task complexity.
D) Include examples with intentionally incorrect labels to improve training.

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

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

3. Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?

A) Softmax similarity search
B) ReLu similarity search
C) Cosine similarity search
D) Sigmoid similarity search

4. What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?

A) To define static rules for retrieving data from the index.
B) To retrieve data based on the user's current session or inputs.
C) To keep the ECS Index stored in a shared Orchestrator folder.
D) To limit the number of results retrieved from the ECS Index.

5. Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?

A) Understanding the daily pain points and inefficiencies of the selected role to identify tasks that consume unnecessary time and potential gains from agent intervention.
B) Generalizing automation opportunities across all processes and roles without tailoring solutions based on specific personas or organizational contexts.
C) Starting immediately with agent behavior prototyping using tools like the Agents designer canvas in Studio Web without assessing mapped automations or impacted systems.
D) Brainstorming automation use cases without validating personas or critically evaluating existing processes, focusing purely on agent capabilities.

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