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[General] UiPath-AAAv1 Certified Questions & Best UiPath-AAAv1 Practice

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【General】 UiPath-AAAv1 Certified Questions & Best UiPath-AAAv1 Practice

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q58-Q63):NEW QUESTION # 58
A business is looking to automate its workflows and has both structured, repetitive tasks (like data entry) and unstructured, exception-heavy processes (such as responding to diverse customer queries). How should they combine agents and robots (RPA) to achieve optimal automation results?
  • A. Use robots (RPA) for the structured, repetitive tasks, leveraging their rule-based approach for reliability and precision, while agents handle the unstructured processes by using their adaptive decision-making capabilities.
  • B. Use agents for the structured, repetitive tasks, as they can follow deterministic rules efficiently while robots (RPA) handle unstructured workflows requiring adaptability, decision-making capabilities and contextual awareness.
  • C. Use robots (RPA) exclusively, as they are capable of adapting to dynamic workflows with exception handling and learning capabilities.
  • D. Use agents exclusively, as they can cover both structured workflows and dynamic environments due to their probabilistic and adaptive nature.
Answer: A
Explanation:
Ais the correct andUiPath-recommended approach:
* RPA botsare ideal forstructured, rule-based, high-volume tasks- like data entry, file manipulation, system integration - wherepredictability and speedare key.
* Agentic AIexcels inunstructured, human-like decision scenarios - likeinterpreting emails,triaging support requests, orresponding to exceptionsusing LLMs and contextual memory.
UiPath promotes ahybrid automation model:
* Letrobotshandle deterministic workflows.
* Letagentsmanage ambiguity, natural language, and decision-making.
* Lethumanshandle escalations or approvals when required.
This createsscalable, intelligent, and efficientworkflows that combine strengths from both systems.
B and C are incorrect because neither agents nor bots alone are sufficient across all use cases.
D reverses the design logic - agents arenotbest for structured tasks; RPA is.
This hybrid approach is foundational in UiPath'sAgentic Orchestration and Co-Pilotstrategies, ensuring right-tool-for-the-taskautomation at scale.

NEW QUESTION # 59
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's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
  • 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 does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
  • 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.
Answer: D
Explanation:
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.

NEW QUESTION # 60
How long does a key-value pair stored in Agent Memory remain available before it expires by default?
  • A. Until the agent version is updated, after which key-value pairs are automatically cleared
  • B. 6 months
  • C. 3 months
  • D. 12 months
Answer: D
Explanation:
Cis correct - according to UiPath documentation,key-value pairs stored in Agent Memorypersist for12 months by default.
Agent Memoryis a persistent storage layer allowing agents to:
* Recall decisions or context across runs
* Store user preferences, status, or temporary flags
* Maintain statefulness without relying on external databases
This capability is especially useful for:
* Omnichannel customer interactions
* Preference-aware recommendations
* Tracking previously taken actions for continuity
Although memory storage is long-lasting (12 months), developers can:
* Manually resetor expire entries
* Use different memory scopes (e.g., per-user, per-agent)
* Design memory-aware flows for personalization
Option D is incorrect - memory isnot auto-cleared on version updates.
A and B understate the retention policy - default expiration is clearly documented as12 monthsunless changed manually.
Agent Memory is a powerful enabler ofcontext-rich, stateful automations, especially for conversational or ongoing interactions.

NEW QUESTION # 61
While configuring an Integration Service activity as a tool for your agent in Studio Web, how should you set up the activity so the agent can decide the value of a required field (e.g. Channel Id) at runtime based solely on instructions in the prompt?
  • A. Change every field, including Channel Id, to Argument because an agent cannot infer any field values without explicit arguments.
  • B. Declare the field as an output argument in Data Manager so the agent can feed a value back into the tool.
  • C. Leave the field's input method on Prompt (the default) and keep or refine the tool description; this lets the agent infer the value during execution.
  • D. Change every field, including Channel Id, to Variable because an agent cannot infer any field values without explicit arguments.
Answer: C
Explanation:
Bis correct - when a field (likeChannel Id) is set toPrompt, the agent will attempt to infer its valueat runtime, based on theinstructions in the promptand the context provided.
This is the default and preferred mode for agent tools when:
* The agent has enough context or memory to decide
* You wantLLM autonomyin filling the field dynamically
* You're using prompt instructions like: "Post to the user's default Slack channel" Option A is incorrect - "Argument" is used when you're passing aspecific variableinto the agent prompt (not inferred).
C misunderstands data flow direction - "Output" is not relevant for input fields.
D is invalid - "Variable" is not the standard method for field inference in this scenario.
This aligns with UiPath'sagent + tools orchestrationmodel usingStudio Web's low-code agent builder.

NEW QUESTION # 62
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 predefined list of actions and commands the agent strictly follows without adaptation or interaction over time.
  • 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.
Answer: A
Explanation:
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.

NEW QUESTION # 63
......
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