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[Hardware] Top features of UiPath UiPath-AAAv1 Exam Practice Test Questions

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【Hardware】 Top features of UiPath UiPath-AAAv1 Exam Practice Test Questions

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q19-Q24):NEW QUESTION # 19
Why is it important to include examples in prompts?
  • A. Including examples guarantees output accuracy without any need for further adjustments or refinements.
  • B. Examples should be omitted to allow the AI to create responses entirely from general knowledge without guidance.
  • C. Including examples should only focus on edge cases while ignoring typical scenarios for better variety in results.
  • D. Carefully chosen examples help guide the agent and improve its ability to generalize across different scenarios.
Answer: D
Explanation:
Dis correct - includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.
UiPath encourages the use of examples for:
* Classification(e.g., labeling sentiment, email categories)
* Transformation tasks(e.g., turning unstructured text into tables)
* Step-by-step instructions(e.g., troubleshooting flows)
These examples serve two purposes:
* Pattern induction: The model picks up on consistent structures or rules used across examples.
* Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.
Best practice:
* Usetypical, real-world examplesrepresentative of the data the agent will encounter.
* Keep formatsclear and consistentacross input-output pairs.
* Pair examples withexplicit instructionsin the system or user prompt.
Option A is flawed - focusing only on edge cases can confuse the model.
B is false - omitting examples forces the LLM to guess the structure, reducing accuracy.
C is misleading - examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.
In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.

NEW QUESTION # 20
How does agentic orchestration ensure consistency and reliability in processes?
  • A. By forcing robots and people to work separately, maintaining a strict division of roles without overlap.
  • B. By significantly reducing the level of human intervention required, confining their involvement to only a minimal fraction of the overall operational processes and decision-making activities.
  • C. By using standard business process modeling notation (BPMN) to define business rules and guardrails for AI agents.
  • D. By allowing agents complete autonomy to make independent decisions based on real-time scenarios.
Answer: C
Explanation:
The correct answer isA- UiPath'sagentic orchestration layerusesBPMN (Business Process Model and Notation)to visually model and govern the workflows in which AI agents operate. This is a core feature of UiPath Maestro, where BPMN ensures:
* Clear definition of rules, handoffs, and agent actions
* Guardrails for decision-making
* Coordination between people, robots, and AI agents
* Reusability and governanceof business logic
Agentic orchestration doesnot mean giving full autonomy to agents(as in D), nor does it aim to eliminate human input entirely (as in B). Instead, it promotesadaptive workflowswhere human review, agent action, and automation co-exist in a governed way.
Option C is incorrect because UiPath specificallyencourages hybrid collaborationbetween humans, bots, and agents. BPMN is the bridge that brings that orchestration to life.

NEW QUESTION # 21
Why is mapping processes a critical step in identifying opportunities for agentic automation?
  • A. It assumes mapping processes is sufficient to complete automation implementation without considering task dependencies or broader workflows.
  • B. It prioritizes identifying potential ROI metrics before establishing specific process mapping, potentially overlooking optimization areas.
  • C. It allows pinpointing specific steps or sub-tasks within a workflow that could be automated, improving efficiency and reducing errors.
  • D. It examines broader workflows without focusing on individual steps, missing granular opportunities for automation.
Answer: C
Explanation:
Cis correct - mapping processes during agentic discovery is essential because it allows teams tozoom into specific tasks or sub-processeswhere agentic automation can deliver the highest value.
UiPath'sAgentic Design Blueprintmethodology emphasizes this as afoundational step. By creating detailed
"as-is" process maps, teams can:
* Spotrepetitive tasks(ideal for RPA)
* Findjudgment-based decisions(ideal for agents)
* Highlightescalation points, delays, and handoffs
This clarity helps identify:
* Which actions can be automated
* Which roles require agent augmentation
* What context (data or documents) is needed
Option A skips process mapping and risks missing real value.
B is too high-level - real insights come from step-level granularity.
D is misleading - mapping is necessary butnot sufficientfor full implementation.
Accurate process mapping creates avisual and logical foundationfor designing agents that integrate seamlessly into workflows - targeting the right problems and unlocking measurable ROI.

NEW QUESTION # 22
An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as
'Positive', 'Neutral', or 'Negative'. Which few-shot design is most helpful?
  • A. Input: "I love the new design, very intuitive!" Output: "Positive"
    Input: "Nothing special, just works." Output: "Neutral"
    Input: "Terrible experience, won't use again." Output: "Negative"
  • B. "Text" Use a multiple-choice table with numerical ratings from 1-5.
  • C. Input: "The app is okay I guess." # Output:
  • D. Options: List words like: "great, okay, bad" and map them to tone.
Answer: A
Explanation:
Dis correct - this example follows thegold standard for few-shot prompting, as defined in UiPath's Prompt Engineering methodology. The format usesclearly labeled input-output pairs, giving the agent:
* Consistent structure to follow
* Explicit tone classification
* Variety across sentiment categories
Each example models the task exactly as it should be performed:
* Input: [Text]
* Output: [Label] (Positive, Neutral, Negative)
This design teaches the agenthow to recognize patterns in user tone, even with subtle expressions. It works especially well in LLM-powered agents that handlefeedback analysis,review classification, orcustomer support automation.
Option A (listing keywords) lacks structure and will not generalize well.
B is incomplete - there's no output for the model to learn from.
C uses a rating scale, which doesn't match the classification labels needed.
UiPath emphasizes thatwell-structured few-shot examplesimprove LLM accuracy dramatically - especially when working with ambiguous or emotionally nuanced language.
This approach improvessentiment classification precision, reduces hallucination, and ensures consistent labeling across varied input phrasing - making the agent more reliable in real-world scenarios.

NEW QUESTION # 23
What is a System Prompt?
  • A. A System Prompt defines only the agent's constraints but does not address goal-setting or sequencing steps.
  • B. 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.
  • 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: B
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 # 24
......
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