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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:
1. Why is an agent story important in the development life-cycle?
A) A poorly defined agent story enables developers to identify improvement opportunities
B) A good agent story helps the developers who will build the agent to focus on the essential features that deliver value
C) An unclear agent story helps SMEs and stakeholders understand the potential risks associated with the agent
D) A detailed agent story is only necessary when showcasing the agent's functionality to key stakeholders, rather than guiding the development process
2. Which persona typically models agentic processes in Maestro with BPMN and governs their full lifecycle?
A) Process operations teams and system admins
B) Automation developers in the Center of Excellence
C) Process owners in business teams
D) Process excellence analysts optimizing performance
3. What is a System Prompt?
A) A System Prompt allows a user to describe its role, goals, and constraints while specifying rules and guidelines for actions, including the use of tools, escalations, and context.
B) A System Prompt is a technical script integrated into the automation process that determines tool usage and escalation protocols without considering natural language descriptions.
C) A System Prompt is a predefined list of actions and commands the agent strictly follows without adaptation or interaction over time.
D) A System Prompt defines only the agent's constraints but does not address goal-setting or sequencing steps.
4. A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?
A) The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
B) The inconsistency is related to the token limit defined for the prompt's length, which affects the LLM's ability to complete a response rather than its understanding of technical terms.
C) The LLM's tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.
D) The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
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) Use random and unrelated examples to test the prompt's robustness.
B) Include examples with intentionally incorrect labels to improve training.
C) Choose examples that are diverse, relevant, and typical of the task's expected input.
D) Always use more than 10 examples, regardless of task complexity.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: C |

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