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[General] UiPath UiPath-AAAv1 Valid Test Cost | UiPath-AAAv1 Exam Cost

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【General】 UiPath UiPath-AAAv1 Valid Test Cost | UiPath-AAAv1 Exam Cost

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q20-Q25):NEW QUESTION # 20
Which of the following is a benefit of UiPath-built agents?
  • A. They allow for quick agent creation using a low-code development application.
  • B. They cannot integrate with UiPath Orchestrator.
  • C. They require extensive coding expertise for development.
  • D. They are limited to handling structured workflows only.
Answer: A
Explanation:
D is correct - a major advantage of UiPath-built agents is their low-code creation model, which allows business users and developers to quickly create, test, and deploy agents.
Key points from UiPath's Agentic Automation platform:
Agents are built in Studio Web, using a drag-and-drop UI and agent designer canvas.
Low-code tools allow teams to design agent prompts, behavior logic, tool connections, and escalations without deep programming skills.
Agents integrate with UiPath Orchestrator for full lifecycle management.
UiPath's low-code stack is designed to:
Lower the barrier to AI adoption
Accelerate time-to-value
Allow cross-functional teams to collaborate on intelligent automation
Options A and B are incorrect - agents support both structured and unstructured workflows, and fully integrate with Orchestrator.
C is false - low-code is a core value prop.

NEW QUESTION # 21
How long does a key-value pair stored in Agent Memory remain available before it expires by default?
  • A. 3 months
  • B. Until the agent version is updated, after which key-value pairs are automatically cleared
  • C. 12 months
  • D. 6 months
Answer: C
Explanation:
Cis correct - according to UiPath documentation,key-value pairs stored in Agent Memorypersist for12 months by default.
Agent Memoryis a persistent storage layer allowing agents to:
* Recall decisions or context across runs
* Store user preferences, status, or temporary flags
* Maintain statefulness without relying on external databases
This capability is especially useful for:
* Omnichannel customer interactions
* Preference-aware recommendations
* Tracking previously taken actions for continuity
Although memory storage is long-lasting (12 months), developers can:
* Manually resetor expire entries
* Use different memory scopes (e.g., per-user, per-agent)
* Design memory-aware flows for personalization
Option D is incorrect - memory isnot auto-cleared on version updates.
A and B understate the retention policy - default expiration is clearly documented as12 monthsunless changed manually.
Agent Memory is a powerful enabler ofcontext-rich, stateful automations, especially for conversational or ongoing interactions.

NEW QUESTION # 22
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. 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"
  • B. Options: List words like: "great, okay, bad" and map them to tone.
  • C. Input: "The app is okay I guess." # Output:
  • D. "Text" Use a multiple-choice table with numerical ratings from 1-5.
Answer: A
Explanation:
Dis correct - this example follows thegold standard for few-shot prompting, as defined in UiPath's Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:
* Consistent structure to follow
* Explicit tone classification
* Variety across sentiment categories
Each example models the task exactly as it should be performed:
* Input: [Text]
* Output: [Label] (Positive, Neutral, Negative)
This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.
Option A (listing keywords) lacks structure and will not generalize well.
B is incomplete - there's no output for the model to learn from.
C uses a rating scale, which doesn't match the classification labels needed.
UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically - especially when working with ambiguous or emotionally nuanced language.
This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing - making the agent more reliable in real-world scenarios.

NEW QUESTION # 23
What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?
  • A. To retrieve data based on the user's current session or inputs.
  • B. To limit the number of results retrieved from the ECS Index.
  • C. To keep the ECS Index stored in a shared Orchestrator folder.
  • D. To define static rules for retrieving data from the index.
Answer: A
Explanation:
Dis correct - the primary purpose of usingContext Grounding with an ECS (Enterprise Context Service) Indexin UiPath'sAgents designer canvasis to enablereal-time, dynamic retrieval of knowledgebased on the current user session or prompt inputs.
ECS indexes are built from documents, FAQs, policies, tickets, or any enterprise content and are used to:
* Provide agents withlive knowledge grounding
* Reduce hallucinations in LLM outputs
* Support tasks like Q&A, decision-making, and summarization
When a user inputs a query, the LLM canreference the ECS indexusing similarity search (usuallycosine similarity) to pullrelevant context chunksinto the prompt. This makes the agent smarter, safer, and more accurate.
Option A relates to deployment, not purpose.
B suggests hard-coded retrieval logic, which is the opposite of dynamic grounding.
C is about tuning, not the core purpose.
Context Grounding allows agents to actcontextually and intelligently, using up-to-date organizational data
- a foundational principle in UiPath's agentic architecture.

NEW QUESTION # 24
When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?
  • A. Whenever you need to paste any content regardless of operating system, file type, or the number of pastes.
  • B. When you are working on a Windows machine and need to perform a single AI-powered paste of a table (for example, from a PDF) into another application directly from the chat interface.
  • C. When you are using macOS and want Autopilot for Everyone to perform a copy and paste on a Linux VM.
  • D. When you plan to paste several different tables in succession during the same chat and expect Autopilot for Everyone to queue each paste automatically.
Answer: B
Explanation:
Cis correct -Clipboard AI, as embedded insideAutopilot for Everyone, is optimized forWindows environments, particularly when performingstructured copy-and-paste operations, such as extracting tables from a PDF and transferring them to Excel, Word, or web forms.
Best-use scenario:
* You copy structured data (like a table or text block)
* Paste it once into theAutopilot chat window
* Ask Autopilot to "paste this into [target app] in a structured format"
* It leverages Clipboard AI's logic to map and format the content intelligently Option A is incorrect - Autopilot doesn't queue multiple pastes. Each interaction is scoped.
B overstates platform independence - current support isWindows-first.
D is incorrect - Clipboard AI doesnot support macOS or cross-VM pastingyet.
This capability helpsnon-technical users automate repetitive copy-paste actions, improving speed, accuracy, and structure when transferring information across applications.

NEW QUESTION # 25
......
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