UiPath-AAAv1試験準備 & UiPath-AAAv1模擬試験試験にすぐに合格する場合は、UiPath-AAAv1準備ガイドが最適です。多くのユーザーは、学ぶ時間があまりないことを知っています。これに対応して、データの内容を科学的に設定しました。断片的な時間を使って学習することができ、1分ごとに効果があります。 UiPath-AAAv1試験問題の内容を本当に吸収できるように、学習計画を調整します。この学習計画は、あなたの仕事や生活にも大きな影響を与える可能性があります。 UiPath-AAAv1学習ガイドを20〜30時間慎重に学習している限り、UiPath-AAAv1試験に進むことができます。 UiPath Certified Professional Agentic Automation Associate (UiAAA) 認定 UiPath-AAAv1 試験問題 (Q36-Q41):質問 # 36
Which persona typically models agentic processes in Maestro with BPMN and governs their full lifecycle?
A. Process excellence analysts optimizing performance
B. Automation developers in the Center of Excellence
C. Process operations teams and system admins
D. Process owners in business teams
正解：D
解説：
The correct answer isD- according to UiPath'sMaestro orchestration framework, theprocess ownerplays a central role in defining and governing agentic workflows.
In UiPath Maestro:
* Process ownersuseBPMN diagramsto map the flow of work, decision points, hand-offs, and automation steps.
* They defineagent boundaries, escalation rules, and success conditions.
* This model empowersbusiness-side expertsto own automation design while working alongside technical teams.
Unlike classic automation that's owned by IT or CoE developers, agentic processes requirebusiness-context awareness, makingprocess ownersessential to managing thefull lifecycle- from design to governance to optimization.
Options A and B refer to support roles. Option C (developers) implement parts of the design, but don't usually govern the lifecycle or own the process vision.
This reflects UiPath's broader push forbusiness-led automation, enabled by Maestro and Autopilot™ in Studio Web.
質問 # 37
What are the primary benefits of Context Grounding when querying data across multiple documents?
A. Context Grounding requires manual intervention for identifying connections between data points across documents.
B. Context Grounding is limited to querying within a single document at a time.
C. Context Grounding only extracts random sentences without contextual understanding.
D. Context Grounding understands relationships between data points across documents, enabling tasks like summarization, data comparison, and retrieval of highly relevant information.
正解：D
解説：
Dis correct -Context Groundingin UiPath usessemantic search across indexed contentto provide relevant and meaningful context to the agent, even when the data spansmultiple documents.
This capability is powered by:
* Embedding-based similarity search(e.g., cosine similarity)
* Intelligent chunking and indexing of enterprise data
* Runtime query matching based on theagent's prompt or user input
This enables agents to:
* Retrieverelevant information across distributed content
* Detectrelationships between topics, even if data is fragmented
* Supportmulti-document summarization,comparison, andknowledge-based reasoning For example, an agent could compare policy details across multiple HR documents to generate a unified response or identify inconsistencies in invoice records spread across different files.
Option A is false -Context Grounding is automaticonce indexing is configured.
B is incorrect - it's explicitly designed toquery across documents.
C misrepresents the system - it doesn't extract random text; it retrievessemantically relevantpassages based on the LLM's intent.
This powerful grounding mechanism makes UiPath agentsintelligent, context-aware, and enterprise-ready, especially in knowledge-intensive environments.
正解：A
解説：
Bis correct - the bpmn.uipath.com canvas is alightweight sandbox environmentfordrafting and visualizing agentic processes, butdoes not include full implementation capabilities. It is part of UiPath's broaderMaestro experience, designed forearly-stage discovery, collaboration, and ideation.
Key characteristics:
* Drag-and-dropBPMN modeling
* Ability tooutline agents, decisions, automations, escalations
* Useful forcollaborating with stakeholdersbefore technical development begins
* Lacksdirect execution, tool integration, or runtime support
It is not a replacement forStudio WeborAutomation Cloud, which are used for:
* Full implementation
* Connecting to tools, prompts, or systems
* Deployment and testing
Option A is incorrect - implementation requires transition intoStudio Web.
C is false - the tool is formodeling, not template import/export.
D misrepresents its role - it'snot the full-featured modeling tool, but adiscovery-phase sandbox.
Best practice: use bpmn.uipath.com todesign collaboratively, then export or map the flow inton8n,Studio, or Maestro production canvasfor build-out and testing.
質問 # 39
How does adjusting the "Number of results" setting affect the agent's use of context from indexes?
A. It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.
B. It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
C. It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.
D. It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
正解：D
解説：
The correct answer isC. In UiPath'sContext Groundingconfiguration, the"Number of results"setting directly affects how manychunks of indexed knowledgeare retrieved and passed to the LLM at runtime.
These chunks come from preprocessed documents and are used to build thegrounding payload- the content added to the agent's prompt for context-aware generation.
By increasing the number of results:
* The LLM has access tomore context, which can improve response quality if the added information is relevant.
* However, it alsoincreases the token load, which can reduce prompt space or introduce irrelevant noise if poorly tuned.
Reducing the number of results leads tomore focused prompts, with only top-ranked relevant chunks (based oncosine similarity) included. This is crucial when using large indexes or when LLM context windows are limited.
Option A confuses this setting with similarity threshold tuning, which is a separate parameter.
Option B is false - the agent doesnot ignore contextunless context grounding is disabled.
Option D misrepresents the function - Orchestrator folder selection is unrelated to this retrieval setting.
In summary, the "Number of results" setting allows fine-tuning ofhow much supporting context is retrieved and passed to the model. It is a key control in optimizing performance, precision, and relevance of grounded agent responses.
質問 # 40
What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
A. Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.
B. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
C. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
D. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
正解：A
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.