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UiPath UiPath-AAAv1 Exam Syllabus Topics:

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

1. A company launches a marketing campaign powered by generative AI and agentic AI technologies. What best describes how their roles differ?

A) Generative AI produces outputs like text or images, while agentic AI focuses on intelligent automation, decision-making, and dynamic task execution.
B) Generative AI focuses on improving customer sentiment analysis, while agentic AI refines campaign content.
C) Agentic AI designs campaign materials, while generative AI handles customer queries in real-time.
D) Both generative AI and agentic AI collaborate to create promotional materials.

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

A) To keep the ECS Index stored in a shared Orchestrator folder.
B) To retrieve data based on the user's current session or inputs.
C) To limit the number of results retrieved from the ECS Index.
D) To define static rules for retrieving data from the index.

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

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

4. A team is designing an agent to convert plain text meeting notes into a formatted agenda (e.g., structured bullet points). Despite providing a few example transformations in the prompt, the agent generates agendas in inconsistent formats. What critical step was likely overlooked?

A) Including constraints to limit the length of the agenda for simplicity.
B) Providing only examples without additional context about the task.
C) Adding randomized formatting examples to test the agent's creativity.
D) Adding clear instructions detailing the output format.

5. 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?

A) Options: List words like: "great, okay, bad" and map them to tone.
B) 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"
C) Input: "The app is okay I guess." # Output:
D) "Text" Use a multiple-choice table with numerical ratings from 1-5.

Solutions: