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

1. In which scenario is a deterministic evaluation more appropriate than a model-graded one?

A) When open-ended reasoning needs to be scored.
B) When the response quality depends on user satisfaction.
C) When evaluating the tone and helpfulness of agent responses.
D) When the correct output is known and fixed.

2. How does adjusting the "Number of results" setting affect the agent's use of context from indexes?

A) It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
B) It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.
C) It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
D) It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.

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

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

5. Why is an agent story important in the development life-cycle?

A) A good agent story helps the developers who will build the agent to focus on the essential features that deliver value
B) A detailed agent story is only necessary when showcasing the agent's functionality to key stakeholders, rather than guiding the development process
C) An unclear agent story helps SMEs and stakeholders understand the potential risks associated with the agent
D) A poorly defined agent story enables developers to identify improvement opportunities

Solutions: