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UiPath UiAAAv1 Exam (page: 1)
UiPath Agentic Automation Associate
Updated on: 12-Jan-2026

Viewing Page 1 of 23
UiAAAv1 PDF 176 Questions

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?

  1. Use random and unrelated examples to test the prompt's robustness.
  2. Always use more than 10 examples, regardless of task complexity.
  3. Choose examples that are diverse, relevant, and typical of the task's expected input.
  4. Include examples with intentionally incorrect labels to improve training.

Answer(s): C

Explanation:

In few-shot prompting, the quality of examples is crucial. They must be diverse, relevant, and representative of the real inputs the agent will process, ensuring the model generalizes correctly to the classification task.



An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:

  1. Retrieve relevant troubleshooting steps from the web.
  2. Notify the user via Slack if a solution is found.
  3. Escalate unresolved tickets via a custom process.
    Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?
  4. Use random input sampling across all tools and rely on the default "LLM-as-a-Judge" assertion.
  5. Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.
  6. Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.
  7. Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than 30 for escalation processes.

Answer(s): C

Explanation:

Grouping evaluations into sets (valid web results Slack notifications, invalid web results escalations, and edge cases) ensures all workflows are covered without redundant tests. This structured approach provides comprehensive coverage while maintaining efficiency.



What are the primary benefits of Context Grounding when querying data across multiple documents?

  1. Context Grounding is limited to querying within a single document at a time.
  2. Context Grounding only extracts random sentences without contextual understanding.
  3. Context Grounding requires manual intervention for identifying connections between data points across documents.
  4. Context Grounding understands relationships between data points across documents, enabling tasks like summarization, data comparison, and retrieval of highly relevant information.

Answer(s): D

Explanation:

Context Grounding allows the agent to understand relationships between data points across multiple documents, which enables advanced tasks such as summarization, comparison, and retrieving highly relevant insights with contextual accuracy.



An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as `Positive', `Neutral', or `Negative'.
Which few-shot design is most helpful?

  1. Input: "The app is okay I guess." Output: "Text"
  2. Input: "I love the new design, very intuitive!"
    Output: "Positive"
    Input: "Nothing special, just works."
    Output: "Neutral"
    Input: "Terrible experience, won't use again."
    Output: "Negative"
  3. List words like: "great, okay, bad" and map them to tone.
  4. Use a multiple-choice table with numerical ratings from 1-5.

Answer(s): B

Explanation:

Providing clear few-shot examples that map full customer feedback texts directly to the sentiment labels ("Positive", "Neutral", "Negative") guides the LLM to consistently produce the correct classification format.



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

  1. Inputs description fields
  2. Assignment recipient list
  3. Agent Memory toggle
  4. Outcome behavior section

Answer(s): D

Explanation:

The Outcome behavior section specifies the agent's next action after a human resolves the escalation, defining how the process continues.



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?

  1. 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.
  2. The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
  3. 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.
  4. The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.

Answer(s): A

Explanation:

LLMs rely on tokenization, and complex technical terms can be split into multiple tokens. This fragmentation can cause slight variations in how the model interprets and relates those terms, leading to inconsistent handling in generated outputs.



What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?

  1. To retrieve data based on the user's current session or inputs
  2. To define static rules for retrieving data from the index
  3. To limit the number of results retrieved from the ECS Index
  4. To keep the ECS Index stored in a shared Orchestrator folder

Answer(s): A

Explanation:

A context grounding strategy with an ECS Index ensures that data retrieval is aligned with the user's current session or inputs, allowing the agent to fetch contextually relevant information during execution.



What is the key difference between a system prompt and a user prompt when configuring an agent?

  1. A system prompt is used for input formatting and passing dynamic arguments, while a user prompt guides the agent's behavior and planning over time.
  2. A system prompt defines the agent's role, goals, rules, and when to use tools or escalate, while a user prompt structures how input arguments are passed to the agent at runtime.
  3. A system prompt and a user prompt both serve the same purpose but are written in different parts of the agent.
  4. System prompts exist solely to keep agents constantly adapting in real time, while user prompts are meant for agents that never change their behavior.

Answer(s): B

Explanation:

The system prompt defines the agent's role, goals, rules, and decision-making framework, including when to use tools or escalate. The user prompt, on the other hand, structures how input arguments are provided to the agent at runtime.



Viewing Page 1 of 23



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