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UiPath Agentic Automation Associate Questions 71–75 (page: 16)

Updated On: 10-Feb-2026

You are designing a few-shot prompt for a chatbot to generate answers based on customer queries. The task involves adding structured examples to the prompt.
What initialization step is critical for ensuring the agent understands the context properly?

  1. Only focusing on the output without aligning examples to input queries.
  2. Ensuring the prompt begins with a clear explanation of the task and includes structured examples.
  3. Adding a detailed system message about the chatbot's role, without needing to include specific inputs.
  4. Starting with an open-ended prompt and allowing the Agent to infer the pattern from real-time inputs.

Answer(s): B

Explanation:

When designing a few-shot prompt, it is critical to begin with a clear explanation of the task and provide structured examples that align inputs with expected outputs. This ensures the agent understands the context and can generate accurate responses.



What distinguishes model-graded evaluations from deterministic ones?

  1. The use a language model to assess the quality of agent responses across multiple dimensions.
  2. They rely solely on matching agent responses to exact ground truth.
  3. They use random selection to assign scores to agent responses.
  4. They require human evaluators for every response.

Answer(s): A

Explanation:

Model-graded evaluations use a language model to assess agent responses across multiple dimensions such as correctness, relevance, reasoning, and style, whereas deterministic evaluations rely on exact matches to a predefined ground truth.



You are evaluating an agent that processes structured input and generates outputs containing exact numerical values, boolean fields, and predefined strings. The outputs must match the expected structure and values precisely for the solution to function correctly.
Which evaluator type is best suited for this scenario?

  1. LLM-as-a-Judge, as it evaluates semantic similarity and reasoning, and you can instruct it to check for an exact match between the input and expected output.
  2. JSON similarity, but only for assessing open-ended natural language responses in complex scenarios.
  3. LLM-as-a-Judge, as it guarantees exact alignment between numerical and boolean fields in structured outputs.
  4. Deterministic evaluators (Exact match or JSON similarity), as they ensure precise alignment between the generated output and the expected structure and values.

Answer(s): D

Explanation:

Deterministic evaluators such as Exact match or JSON similarity are best for scenarios requiring precise alignment of numerical values, boolean fields, and predefined strings with the expected output, ensuring functional correctness.



What is one key characteristic of Generative AI that does not apply to Large Language Models (LLMs)?

  1. Generative AI specializes exclusively in text-based tasks like document summarization and question answering.
  2. Generative AI focuses on creating various types of content, such as images, audio, and videos, beyond just text.
  3. Large Language Models operate primarily in domains unrelated to natural language processing.
  4. LLMs are designed specifically for generating multimedia including 3D interactive elements.

Answer(s): B

Explanation:

A key characteristic of Generative AI is its ability to create diverse types of content - including images, audio, and videos - not just text. This distinguishes it from LLMs, which primarily focus on text-based generation and understanding.



In UiPath's agentic orchestration, how do robots, agents, and humans collaborate to optimize automation workflows?

  1. All three components work independently without any coordination, each handling separate business processes.
  2. Agents replace both robots and humans entirely, eliminating the need for any human intervention or robotic process automation.
  3. Humans must manually program every action for both robots and agents, making real-time decisions for each automated step.
  4. Robots execute rule-based tasks, AI agents handle cognitive processes and decision-making, while humans provide strategic provide oversight and exception handling, creating a seamless collaborative automation ecosystem.

Answer(s): D

Explanation:

In UiPath agentic orchestration, robots handle rule-based tasks, AI agents manage cognitive and decision- making tasks, and humans provide strategic oversight and handle exceptions. This collaboration optimizes automation workflows by leveraging the strengths of each component.



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