UiPath-AAAv1 Sample Questions & Answers
Mapping agent tasks and goals through blueprint design carries the most weight, next to crafting prompts, basic AI and language model ideas, judging which processes suit automation, grounding context, escalations to a human, agent evaluation, and platform integrations.
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- Question 1Advanced
Agentic Evaluations · Differentiate between deterministic and model-graded evaluations
You are designing an evaluation strategy for a 'Customer Email Reply' agent. You want to ensure the agent's responses are not only factually correct based on the provided context but also maintain a 'Professional and Empathetic' tone. Which combination of evaluation methods is most appropriate?
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Correct answer: D
Tone (Professional/Empathetic) is subjective and linguistic, requiring an LLM (Model-graded) to evaluate effectively. Factual correctness based on context (RAG) is also best evaluated by a Model-graded approach (comparing answer to source context), as strict string matching (Deterministic) would fail if the wording differs slightly.
- Question 2Beginner
Prompt Engineering · Design few-shot structured prompts
When engineering a prompt for a complex data extraction task, you notice the model occasionally hallucinates fields that don't exist. You decide to include three examples of correct input-output pairs within the prompt to guide the model. What is this prompting technique called?
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Correct answer: B
Few-shot prompting involves providing a small set of examples (shots) in the prompt to demonstrate the desired behavior, format, or logic to the model. This is highly effective for enforcing structure and reducing hallucinations.
- Question 3Advanced
UiPath Platform Components and Integrations · Design input/output schema for tools
You are creating a tool in UiPath Studio Web for an agent to look up order status. The underlying API returns a complex JSON object with 50 fields, but the agent only needs the 'Status' and 'DeliveryDate'. How should you design the tool's Output Schema to optimize the agent's performance and token usage?
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Correct answer: A
Best practice for Agent tools is to minimize the context window usage and cognitive load. By defining a strict schema with only relevant fields, you reduce token costs and prevent the agent from getting distracted by irrelevant data.
- Question 4Intermediate
Agentic Orchestration with UiPath Maestro · Configure Service Tasks to invoke agents with 'Start and wait for agent'
In UiPath Maestro, you are modeling a business process using BPMN. You have a task where an AI Agent needs to draft a response to a customer, but the process cannot proceed until the draft is created. Which configuration should you apply to the Service Task representing the Agent?
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Correct answer: C
To ensure the process waits for the Agent's output (the draft) before moving to the next step, the 'Start and wait for agent' configuration is required. This makes the execution synchronous regarding the process flow.
- Question 5Beginner
Autopilot for Everyone · Describe when Autopilot for everyone should be used
Which of the following scenarios is the most appropriate use case for 'Autopilot for Everyone'?
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Correct answer: B
Autopilot for Everyone is designed for personal productivity tasks, ad-hoc assistance, and simplifying daily activities for individual business users, such as summarization and content generation.
- Question 6Intermediate
Agentic AI and Agentic Automation Concepts · Articulate the benefits of agentic automation
You are identifying potential automation opportunities within a Supply Chain department. You find a process: 'Vendor Risk Assessment'. It involves reading market news (highly variable), correlating it with internal vendor history (structured DB), and producing a risk score. The logic for the score changes weekly based on geopolitical events. Why is this a better fit for an Agent than a Robot?
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Correct answer: D
Key differentiator: Adaptability and Unstructured synthesis. Robots need fixed rules ('if X then Y'). Agents can handle 'Evaluate risk based on current news', adapting to the content of the news without rule reprogramming.
- Question 7Beginner
Agentic Discovery · Design agent stories through the 5 characteristics
During the 'Agentic Discovery' phase, you are defining the 'ADD' (Agent Design Document) characteristics for a new agent. Which of the following elements is NOT one of the 5 core characteristics of an agent story?
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Correct answer: A
The 5 characteristics are: Trigger, Instructions, Context, Human-in-the-Loop, and Tools. Network Topology is an infrastructure concern, not a core characteristic of the agent story definition.
- Question 8Advanced
Agent Blueprint Design · Select the tools appropriate for agent objectives
A developer is building a 'Procurement Assistant' agent. The agent needs to verify vendor tax IDs using a government website. The website has a CAPTCHA and changes its layout frequently. What is the most robust way to provide this capability to the agent?
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Correct answer: D
Agents are not optimized for solving CAPTCHAs or handling complex, fragile UI interactions directly. The robust pattern is to use a specialized RPA robot (which has Computer Vision/UI automation capabilities) as a tool. The Agent calls the Robot, the Robot does the hard UI work, and returns the result.
- Question 9Advanced
Context Grounding · Describe and use Context grounding
You are defining the 'Context' for a Legal Contract Review agent. The source data consists of 10,000 executed contracts stored in SharePoint. You need the agent to answer questions like 'What is the standard liability clause in our 2023 contracts?'. Which strategy is most effective?
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Correct answer: D
RAG (Retrieval-Augmented Generation) via Context Grounding is the standard, scalable solution for querying large document sets. Fine-tuning is expensive and static; RAG allows dynamic retrieval of specific relevant chunks (like liability clauses) from the live or indexed data.
- Question 10Advanced
Agent Blueprint Design · Map business process steps to agent tasks
Case Study: A telecommunications company wants to automate their 'Service Outage Notification' process.
Current State: When a monitoring system detects a network node failure, it sends an alert code (e.g., 'NODE_99_DOWN') to a shared inbox. A human operator reads the code, looks up the affected customer list in a CRM (SQL database), drafts a polite email apologizing for the outage, and sends it to the affected customers.
Requirement: Automate the process but ensure that high-priority enterprise customers receive a personalized email that mentions their specific SLA terms, which are stored in PDF contracts.
Which combination of technologies and agentic design is best suited for this?
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Correct answer: A
This addresses all requirements: The Agent handles the logic and personalization. The SQL Tool handles the structured CRM data. Context Grounding handles the unstructured PDF SLA terms. The LLM handles the polite drafting. Pure RPA fails on the personalized SLA extraction from PDFs and dynamic drafting.
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