UiPath UiPath-AAAv1試験攻略: UiPath Certified Professional Agentic Automation Associate (UiAAA) - ShikenPASS試験ツールの保証

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UiPath UiPath-AAAv1 Exam Syllabus Topics:

Section	Objectives
Agentic Orchestration (Maestro)	- BPMN-based process design - Workflow orchestration with agents
Context Grounding (RAG)	- Retrieval-Augmented Generation concepts - Data grounding strategies
Escalations & Human-in-the-Loop	- Action Center workflows - Exception handling and escalation patterns
UiPath Platform Components	- Agent Builder and Orchestrator basics - Studio Web and Autopilot
Agentic Evaluations & Governance	- Guardrails and validation logic - LLM-as-a-judge metrics
Agent Blueprint Design	- Workflow decomposition - Agent architecture design
Autopilot for Everyone	- AI-assisted automation building - Use cases and capabilities
Agent Discovery & Process Assessment	- Identifying automation opportunities - Process suitability for agentic automation
Prompt Engineering for Agents	- System prompts and constraints - Zero-shot and few-shot prompting
Agentic AI Fundamentals	- AI agents vs rule-based automation - Agentic automation concepts

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UiPath Certified Professional Agentic Automation Associate (UiAAA) 認定 UiPath-AAAv1 試験問題 (Q51-Q56):

質問 # 51
What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?

A. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
B. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
C. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
D. Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.

正解：D

解説：
Bis correct - creating a newevaluation setin UiPath involves a multi-step process designed to enable qualitative and quantitative review of agent behavior.
Steps include:
* Namingthe evaluation set
* Addinginput promptsandexpected outputs
* Saving each test item (often called "evaluations")
* Assigning evaluators, who will manually or automatically score the results This process enablestestable, repeatable evaluationof agent behavior before deployment - ensuring the model produces correct, useful, and safe outputs.
Options A and C are incorrect:
* A reverses the order: inputs and expected outputs are neededbeforeevaluators.
* C is false - evaluation setscan be built from scratch.D implies scoring is automatic, but human reviewers or comparison logic are often required for nuanced evaluations.
This aligns with UiPath's best practices inagent validationand post-deployment assurance.

質問 # 52
What is a System Prompt?

A. A System Prompt is a predefined list of actions and commands the agent strictly follows without adaptation or interaction over time.
B. A System Prompt defines only the agent's constraints but does not address goal-setting or sequencing steps.
C. 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.
D. A System Prompt is a technical script integrated into the automation process that determines tool usage and escalation protocols without considering natural language descriptions.

正解：C

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 # 53
Why is it essential to provide a focused description and usage guidance when adding a tool for an agent?

A. It ensures the agent understands the tool's purpose and can use it effectively in relevant scenarios.
B. It limits the agent's actions to only those explicitly allowed by the user prompt, preventing incorrect reasoning.
C. It guarantees that agents can access and modify any business application data, even without tool integration.
D. It allows agents to execute all types of actions automatically, including Context Grounding and Escalations.

正解：A

解説：
The correct answer isA- in UiPath's Agent Builder (Studio Web), when you add atool(e.g., Integration Service activity, process, API call), it's essential to include aclear description and usage instructions. This description serves as aguide for the LLM, helping it understand:
* What the tool does
* When to use it
* What input/output fields are relevant
Agents rely on this metadata todecidewhen and how to call the tool appropriately during execution. For example, if a tool is meant to send a Slack message, the description should say:
"Use this tool to notify the support team when a high-priority ticket is detected." Without a clear tool description, agents may:
* Misuse tools (e.g., calling the wrong one)
* Fail to act when they should
* Deliver inconsistent results due to lack of grounding
Option B is incorrect - tool access doesn't grant unrestricted data privileges.
C is too narrow - the prompt alone doesn't restrict reasoning; the tool description plays a key role.
D is false - tool execution depends on agent reasoning and prompt logic, not automatic access.
Adding focused usage guidance ensuressafe, relevant, and accurate tool invocation, which is essential in agentic workflows that combine LLM flexibility with enterprise-grade precision.

質問 # 54
Which of the following is a benefit of UiPath-built agents?

A. They require extensive coding expertise for development.
B. They cannot integrate with UiPath Orchestrator.
C. They are limited to handling structured workflows only.
D. They allow for quick agent creation using a low-code development application.

正解：D

解説：
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.

質問 # 55
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. Adding randomized formatting examples to test the agent's creativity.
B. Including constraints to limit the length of the agenda for simplicity.
C. Adding clear instructions detailing the output format.
D. Providing only examples without additional context about the task.

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 # 56
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