信頼できるUiPath-AAAv1日本語サンプル一回合格-権威のあるUiPath-AAAv1認定デベロッパーUiPath-AAAv1最新の試験トレントは、資格試験ごとに分類が異なるため、ユーザーはユーザーの実際のニーズに応じて独自の学習モードを選択できます。 UiPath-AAAv1試験の質問は、ユーザーが選択できるさまざまな学習モードを提供します。これは、コンピューターや携帯電話の複数のクライアントがオンラインで勉強したり、オフライン統合のためにデータを印刷したりするために使用できます。手頃な価格と実践を完璧にサポートする最新のUiPath-AAAv1試験のトレントは、UiPath-AAAv1試験の質問のみを気に入っています。 UiPath Certified Professional Agentic Automation Associate (UiAAA) 認定 UiPath-AAAv1 試験問題 (Q52-Q57):質問 # 52
When creating an Action app, what is the purpose of defining the "Approve" and "Deny" outcomes within the Action schema?
A. To save user input as mandatory action schema properties during automation execution.
B. To guide the agent's next steps based on the review results of Input/Output properties.
C. To dynamically update user-facing form labels with the action result.
D. To ensure the app validates search results and prevents faulty submissions.
正解：B
解説：
The correct answer isB- defining outcomes like"Approve"and"Deny"within an Action schema is critical for guiding downstream logic in agent behavior, especially in scenarios involvinghuman-in-the-loop reviews.
According to UiPath's documentation forAction Center, outcomes act asexplicit decision points. When a user completes a review (e.g., a document, output, or classification), the selected outcome drives what the agent or automation should do next - for example:
* "Approve"might trigger further processing or submission.
* "Deny"could lead to rework, escalation, or termination of the process.
This is especially relevant inagentic workflows, where the agent offloads uncertain tasks to humans, and the human response informs the next step via outcome-driven branching logic.
Options A, C, and D refer to unrelated features like data validation, mandatory fields, or UI tweaks - none of which define thelogical consequencesthat outcomes control.
質問 # 53
An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
* Retrieve relevant troubleshooting steps from the web.
* Notify the user via Slack if a solution is found.
* Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?
A. Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.
B. Use random input sampling across all tools and rely on the default "LLM-as-a-Judge" assertion.
C. Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.
D. Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than
30 for escalation processes.
正解：A
解説：
Cis correct - UiPath recommends structuringagent evaluationsaroundfunctional setsthat align with expected behavior and edge conditions. This strategy:
* Validatesend-to-end logic, not just isolated tool usage
* Helps assess whethertool combinationswork as designed
* Supportstraceable diagnosisof failures or regressions
In this scenario:
* Set 1: Valid Web Search results#Slack notification (success path)
* Set 2: Failed/irrelevant Web Search#Escalation (fallback path)
* Set 3: Edge cases (e.g., ambiguous input, multiple valid matches)
This avoids theredundancyandvolume bloatseen in options B and D.
Option A is too loose - relying solely on random inputs and "LLM-as-a-Judge" introduces risk ofincomplete testing.
Grouping byreal-world interaction patternsmirrors how agents behave in production. It ensures high coverage while keeping evaluation efficient, consistent, andtightly aligned with business logic.
質問 # 54
You are part of a Procurement team that often struggles with manually reviewing and comparing quotations from different vendors. This process is time-consuming, prone to human errors, and lacks real-time price validation. Keeping up with internal rules and market standards makes things even more difficult. This can cause problems and cost overruns. How agents can help?
A. Agents only store vendor quotations without cross-verifying prices, researching market trends, or checking compliance with policies, leaving procurement officers to manually manage the entire validation process.
B. Agents rely on preloaded prices set by vendors and do not research market rates, verify compliance, or provide detailed validation, leading to potential errors and inefficiencies during quotation reviews.
C. Agents automate price validation by extracting item details from quotations, use tools to research market prices, checking policy compliance, and cross-verifying prices against benchmarks before sharing results with procurement officers for better decision-making.
D. Agents focus on sending reminders for deadlines but do not automate price analysis, extract item details, or validate compliance with internal rules, slowing down decision-making for procurement officers.
正解：C
解説：
Cis correct - agents in UiPath canintelligently automate complex procurement workflowsby combining tools likedocument extraction,web search for price benchmarks,policy validation, andLLM-based reasoning.
In this use case:
* The agent extractsstructured data(item, price, quantity) from multiple quotations
* Compares prices withexternal market sourcesusingWeb Searchor integrated APIs
* Appliescompany policies or thresholdsusing system prompts and guardrails
* Flags anomalies, escalates exceptions, or provides summarized comparisons This reduces:
* Manual effort
* Human error
* Turnaround time for approvals
And increases:
* Policy compliance
* Market alignment
* Decision speed for procurement officers
Options A, B, and D all fall short of UiPath agent capabilities. These responses describepassive or limited automations, whereas agents are built to operateproactively and contextually, especially in high-value business functions like procurement.
This example reflects theagentic automation blueprintat work - combining perception, decision, and action across multiple systems in real time.
質問 # 55
A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?
A. The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
B. The inconsistency is related to the token limit defined for the prompt's length, which affects the LLM's ability to complete a response rather than its understanding of technical terms.
C. The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
D. The LLM's tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.
正解：D
解説：
Cis correct - LLMs like those used in UiPath's Agentic Automation rely heavily ontokenization, which breaks input text into subword units (tokens). When complex technical terms (e.g., "UiPath.Orchestrator.
API") aresplit across multiple tokens, the model may not interpret themconsistently or accurately, especially if:
* They're rare or domain-specific
* Appear in different token contexts
* Are inconsistently represented in training data
This is a common challenge in fine-tuning LLMs fortechnical documentation, where small changes in tokenization can shift meaning or relevance weighting. It's why UiPath emphasizesprompt engineeringand context groundingto mitigate misinterpretation.
A is incorrect because thetoken limitaffects response length, not term understanding.
B is misleading - frequency matters, butsemantic relationshipsalso influence interpretation.
D is factually wrong - LLMs absolutely rely on tokenization and arenot rule-basedwith pre-programmed definitions.
Understanding how tokenization impacts prompt fidelity is critical when building agents that use LLMs to generatestep-by-step or technical outputs.
質問 # 56
When you want a connector field value to be inferred dynamically at run time, which input method should you select in the activity tool?
A. Prompt
B. Static value
C. Clear value
D. Argument
正解：D
解説：
The correct answer isD- selecting"Argument"allows a field value in an activity (such as a connector or tool call) to bedynamically inferred at runtime, based on variables, agent state, or previous node outputs.
UiPath Autopilot™ and Studio Web use the"Argument"option inactivity configurationto passdynamic values, especially in agentic workflows where:
* Outputs of one step must inform inputs of the next
* Contextual reasoning or prompt outputs need to feed tool parameters
* Escalation decisions or classifications affect API calls or record updates This is fundamental in making agent behavioradaptive and responsive to user context- a key trait of UiPath's agentic orchestration layer.
Other options:
* A (Static value) is hardcoded
* B (Clear value) wipes any existing input
* C (Prompt) is used when engaging the LLM, not connectors