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:

Question 1

What is the defining characteristic of few-shot prompting?

A. It relies on intermediate reasoning steps to guide the model's response.
B. It uses several examples to help the model understand the task better.
C. It links multiple prompts together in a sequential workflow.
D. It requires the model to generate a response with no examples or instructions.

Question 2

Which configuration area defines what the agent should do after a human resolves the escalation?

A. Inputs description fields
B. Agent Memory toggle
C. Outcome behavior section
D. Assignment recipient list

Question 3

What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?

A. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
B. Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.
C. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
D. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.

Question 4

Why is goal-oriented execution important in autonomous systems?

A. It aligns actions and processes with predefined objectives effectively.
B. It focuses more on adapting tasks randomly rather than achieving goals.
C. It ensures that all tasks are equally prioritized without regard for outcomes.
D. It prioritizes quick execution over producing quality results.

Question 5

What is a key feature of zero-shot prompting?

A. This is necessary for complex or nuanced scenarios.
B. It requires at least one example in the prompt for efficient completion.
C. The model performs tasks without prior examples or training specific to the request.
D. It ensures the model has been fine-tuned for all tasks it encounters.

Solutions: