信用のあるUiPath UiPath-AAAv1認定試験の問題集

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UiPath UiPath-AAAv1 Exam Syllabus Topics:

Section	Objectives
Agentic Discovery	- Identifying Automation Opportunities 1. Agent suitability assessment 2. Use case discovery 3. Business process evaluation
Agentic Evaluations	- Evaluation and Optimization 1. Reliability and governance 2. Evaluation strategies 3. Performance measurement
Context Grounding and Escalations	- Enterprise-Ready Agent Design 1. Data source integration 2. Human-in-the-loop escalation 3. Context grounding
Prompt Engineering	- Prompt Design Techniques 1. Prompt optimization 2. Structured prompting 3. LLM interaction best practices
Agentic AI and Automation Concepts	- Foundations of Agentic Automation 1. UiPath Agentic Automation ecosystem 2. AI and automation fundamentals 3. Human, robot, and agent collaboration
Agent Blueprint Design	- Designing Intelligent Agents 1. Prompt and instruction design 2. Goal and workflow definition 3. Agent orchestration concepts

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UiPath Certified Professional Agentic Automation Associate (UiAAA) 認定 UiPath-AAAv1 試験問題 (Q16-Q21):

質問 # 16
Which of the following best describes how agents handle dynamic environments?

A. Agents rely solely on static rules without contextual learning.
B. Agents adapt to changing conditions by learning.
C. Agents fail to execute tasks when information or processes change.
D. Agents require complete human assistance whenever processes change.

正解：B

解説：
Bis correct - one of the defining strengths ofUiPath's agentic automationis the ability for agents toadapt to dynamic environmentsusingLLMs and contextual grounding.
Agents differ from traditional RPA bots in that they:
* Interpret natural language
* Reason across structured and unstructured data
* Adjust outputs based onreal-time context, grounding, and updated knowledge When processes change - such as updates to escalation rules, variations in incoming requests, or new product names - agents can adjust without reprogramming, thanks to:
* Flexible prompts
* Grounded context from indexes or memory
* Few-shot or zero-shot inference capabilities
This adaptability makes agents ideal for scenarios likeemail triage,customer service, orknowledge work, where inputs and conditions vary.
Option A and D falsely suggest agents are rigid or fully dependent on human intervention.
Option C applies to classic RPA bots - not LLM-powered agents.
While agents don't"learn"in the ML retraining sense during execution, theydynamically interpret and adapt within the context of each session - a key feature enabled by UiPath's Autopilot™, Context Grounding, and agent memory frameworks.
This flexibility is foundational to deploying agents in environments whererules evolve, data flows shift, or human-like understanding is needed.

質問 # 17
What type of agents can be invoked using the 'Start and wait for external agent' feature in UiPath Maestro?

A. Only UiPath Orchestrator robots.
B. Agents that do not require any input or output variables.
C. External agents like Salesforce or ServiceNow.
D. Agents configured exclusively within the same project.

正解：D

解説：
Cis the correct answer - the"Start and wait for external agent"feature in UiPath Maestro is used toinvoke another agentthat has been configured within thesame project or automation environment.
This enables:
* Agent-to-agent chaining
* Modular designwhere complex tasks are offloaded to specialized agents
* Return of results or outputs, once the external agent completes its task Agents must be:
* Properly configured
* Input/output ready
* Available within the orchestration context of the same solution
Option A is incorrect - this feature is about agents, not robots.
B is wrong - external platforms like Salesforce are accessed via connectors,not as agents.
D is false - input/output parameters can and often should be used between agents.

質問 # 18
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. Providing only examples without additional context about the task.
B. Including constraints to limit the length of the agenda for simplicity.
C. Adding clear instructions detailing the output format.
D. Adding randomized formatting examples to test the agent's creativity.

正解：C

解説：
This is a repeat of Question 16, and the correct answer remains A.
Even when few-shot examples are included, omitting clear formatting instructions leads to inconsistent outputs, which can break downstream processes in agentic automation.
UiPath's Prompt Engineering guidance emphasizes that instruction clarity is as important as examples - especially when output format matters (like agendas, classifications, or structured text).
An optimal prompt includes:
A task description (e.g., "Convert meeting notes into a 3-section agenda") Clear format instructions (e.g., use bullet points, bold headers) Few-shot examples Optional constraints like length or tone Without that first element - clear instructions - the LLM has to guess the output format, leading to variance and unreliability.

質問 # 19
What is a System Prompt?

A. A System Prompt allows a user to describe its role, goals, and constraints while specifying rules and guidelines for actions, including the use of tools, escalations, and context.
B. A System Prompt defines only the agent's constraints but does not address goal-setting or sequencing steps.
C. A System Prompt is a technical script integrated into the automation process that determines tool usage and escalation protocols without considering natural language descriptions.
D. A System Prompt is a predefined list of actions and commands the agent strictly follows without adaptation or interaction over time.

正解：A

解説：
Cis the correct answer - in UiPath's Agentic Automation framework, theSystem Promptis acrucial configuration elementthat defines theagent's identity, objectives, behavioral rules, and tool usage logic.
It typically includes:
* Agent Role: e.g., "You are a procurement assistant"
* Goals: "Classify, summarize, or validate supplier quotes"
* Constraints: e.g., "Don't exceed 100 words", "Only use escalation when criteria X is met"
* Tool Usage: "Use Slack tool to notify team if X occurs"
* Escalation Logic: "Escalate to human if confidence is below threshold"
* Context Integration: "Use grounded context from ECS Index when available" This helps the LLM behaveconsistentlyandtransparently, even in unpredictable or complex workflows. It also acts as thestarting configurationfor the agent - informing every decision it makes during runtime.
Option A is incorrect - System Prompts are written innatural language, not code.
B is false - they allow fordynamic adaptation, especially when used with memory and tools.
D is incomplete - the system promptdoes covergoals, constraints, and sequencing of steps.
Bottom line: theSystem Prompt is the "brain" behind the agent, telling it what to do, how to do it, when to act, and when to escalate - all in anatural language-driven, declarative format.

質問 # 20
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.

正解：A

解説：
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.

質問 # 21
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