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Free UiAAAv1 Exam Braindumps
UiPath Agentic Automation Associate Questions 51–55 (page: 12)

Updated On: 10-Feb-2026
Page 12 of 37
PDF with 176 Questions

Which advantage of providing Retrieval-Augmented Generation (RAG) as a service to UiPath generative-AI experiences most directly mitigates the limitations of an LLM's context window?

  1. RAG speeds up document ingestion by caching previous user queries instead of consulting any knowledge base.
  2. RAG permanently removes all context-window limits for any present of future language model, regardless of its architecture or size.
  3. RAG uploads the entire knowledge base into the LLM's prompt, effectively bypassing the need for selective retrieval.
  4. RAG pulls only the most relevant snippets from indexed knowledge bases, thereby improving accuracy, reliability, and efficiency even when the LLM's context window is small.

Answer(s): D

Explanation:

RAG mitigates LLM context window limits by retrieving only the most relevant snippets from indexed knowledge bases, ensuring accurate, reliable, and efficient responses without overloading the model's limited context capacity.



A team is building an AI agent that drafts personalized marketing emails. The quality of the drafts depends on tone, alignment with brand voice, and personalization.
What evaluation approach is best?

  1. Model-graded evaluation to capture nuanced style and relevance.
  2. Deterministic evaluation using a checklist of key phrases.
  3. Random sampling with A/B testing.
  4. Evaluation using a character count threshold to assess message quality.

Answer(s): A

Explanation:

Model-graded evaluation (LLM-as-a-Judge) is best for assessing nuanced aspects like tone, brand voice alignment, and personalization, which cannot be reliably measured with deterministic or rule-based methods.



Which of the following is a key configuration step when using `Start and wait for agent' in UiPath?

  1. Setting up a parallel gateway to handle multiple agents simultaneously.
  2. Using a subprocess to encapsulate the agent logic.
  3. Configuring the agent specifying input/output variables.
  4. Configuring a timer event to trigger the agent.

Answer(s): C

Explanation:

When using `Start and wait for agent', it is essential to configure the agent with the required input and output variables so data can be passed correctly into and out of the agent during execution.



What happens when you set the input method of an activity to Agent (the default choice)?

  1. The agent prompts a human in Action Center to supply the value
  2. The field always applies one constant that you enter during design
  3. The agent derives the value at runtime from the field's Description and its own reasoning
  4. The field uses a workflow argument that was defined earlier in the job

Answer(s): C

Explanation:

When an activity's input method is set to Agent (default), the agent uses the field's Description and its reasoning at runtime to dynamically determine the appropriate value.



While adding the Slack Send Message connector action as a tool in Studio Web, you want the agent-not the workflow designer- to decide the value of the Message field each time it runs.
Which configuration best enables this behaviour?

  1. Keep the field's input method on Prompt and, if needed, fine-tune the tool's description so the agent can infer the value at runtime.
  2. Change the input method to Variable and rely on an undeclared variable, assuming the agent will still infer the text.
  3. Change the input method to Argument and rely on an undeclared variable, assuming the agent will still inter the text.
  4. Convert every field in the activity to Argument because agents cannot infer any field values without explicit arguments.

Answer(s): A

Explanation:

Keeping the field's input method on Prompt allows the agent to infer the value dynamically at runtime. Refining the tool's description improves the agent's ability to determine the correct message content for each execution.



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