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[General] UiPath-AAAv1 Question Explanations - Updated UiPath-AAAv1 Demo

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【General】 UiPath-AAAv1 Question Explanations - Updated UiPath-AAAv1 Demo

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q21-Q26):NEW QUESTION # 21
Which of the following is a benefit of UiPath-built agents?
  • A. They are limited to handling structured workflows only.
  • B. They cannot integrate with UiPath Orchestrator.
  • C. They allow for quick agent creation using a low-code development application.
  • D. They require extensive coding expertise for development.
Answer: C
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 # 22
What are the characteristics of an agentic story within the 'Do later' quadrant in the impact and feasibility matrix?
  • A. High feasibility and Low Impact
  • B. Low feasibility and Low Impact
  • C. High feasibility and High Impact
  • D. Low feasibility and High Impact
Answer: A
Explanation:
Cis correct - an agentic story that falls into the"Do Later"quadrant typically representshigh feasibility but low impact.
In UiPath'sImpact vs. Feasibility Matrix, used during theAgentic Discoveryphase, automation ideas are evaluated on:
* Feasibility(ease of implementation)
* Impact(business value, time saved, ROI)
Quadrants:
* Quick Wins: High impact, high feasibility
* Do Later: Low impact, high feasibility
* Strategic Bets: High impact, low feasibility
* Avoid/Backlog: Low on both
'Do Later' agentic stories are often simple to automate but don't deliver meaningful outcomes - e.g., automating low-volume tasks or internal reports with limited audience.
Focusing onimpactful use casesensures agent development time translates to real business value - one of the key lessons from UiPath's agentic blueprint methodology.

NEW QUESTION # 23
When is it appropriate to rely on Clipboard AI inside Autopilot for Everyone for a copy-and-paste task?
  • A. 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.
  • B. Whenever you need to paste any content regardless of operating system, file type, or the number of pastes.
  • C. When you plan to paste several different tables in succession during the same chat and expect Autopilot for Everyone to queue each paste automatically.
  • D. When you are using macOS and want Autopilot for Everyone to perform a copy and paste on a Linux VM.
Answer: A
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 # 24
When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?
  • A. Automatic conversion of CSV data into native XLSX files for enhanced compatibility.
  • B. Support for structured queries tailored specifically for CSV data.
  • C. Streaming support for real-time ingestion and CSV query execution without indexing.
  • D. Embedding data from CSV files into JSON templates for improved semantic similarity.
Answer: D
Explanation:
Dis correct - UiPathContext Groundingsupports querying unstructured and semi-structured data, including CSV files, by embedding their content intosemantic representationssuch asJSON-formatted chunksduring indexing.
Here's how this works for CSVs:
* UiPathparses the tabular dataand maps each row or section into asemantically rich format(e.g., JSON)
* These JSON-structured embeddings are then stored inECS Indexes(Enterprise Context Store)
* When an LLM agent queries the index, it retrieves themost contextually relevant data, even across large datasets This unlocks:
* Smarter question answeringfrom tabular data
* Cross-referencing multiple fieldsin a single query
* Enhanced LLM understanding by transforming flat rows intorelational, structured prompts Option A is misleading - LLMs rely onsemantic similarity, not SQL-like structured queries.
B is false - CSV is not auto-converted into XLSX.
C is incorrect - streaming is not yet supported; indexing is a prerequisite.
In short, UiPath enablessemantic grounding of structured datalike CSVs by reformatting them intoJSON- style embeddings, improving retrieval quality, summarization, and task-specific use cases.

NEW QUESTION # 25
For what primary reason should you supply a description for every input and output argument in an agent?
  • A. Descriptions cause Orchestrator triggers to pre-populate the arguments automatically, eliminating manual mapping.
  • B. Clear descriptions help the agent understand how to use each argument effectively while generating or returning results.
  • C. Adding descriptions forces Studio Web to treat all arguments as mandatory fields that block deployment if left empty.
  • D. Argument descriptions are required only for input arguments; output arguments are inherently self- explanatory and do not benefit from them.
Answer: B
Explanation:
Bis the correct answer - in UiPath's Agent Builder (Studio Web),descriptions for input and output arguments serve as grounding contextfor the agent. These descriptions help the LLMunderstand what each argument represents, how it should be used in the generation process, and how to structure its outputs.
This is especially critical for:
* Inputs like {{CUSTOMER_ISSUE}} - the agent needs to know it's a complaint, question, or error
* Outputs like {{TROUBLESHOOTING_STEPS}} - the agent should format these as steps, not just a summary These descriptions:
* Improve theaccuracy of prompt generation
* Ensure the agentreturns structured, expected data
* Help guide LLM behavior in multi-step or dynamic workflows
Option A is incorrect - Orchestrator triggers donot auto-mapbased on descriptions.
C is false - descriptions donot make arguments mandatory.
D is incorrect -output arguments benefit greatly from descriptions, especially for guiding LLMs on return format and content.

NEW QUESTION # 26
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