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[General] Exam UiPath-AAAv1 Braindumps & UiPath-AAAv1 Free Braindumps

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【General】 Exam UiPath-AAAv1 Braindumps & UiPath-AAAv1 Free Braindumps

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UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions (Q33-Q38):NEW QUESTION # 33
A company is integrating an Agent into its customer support workflow to detect sentiment and classify complaints (e.g., "Billing issue", "Product defect"). However, the Agent's responses often miss subtle emotional cues like frustration or urgency. What change to the prompt design would most improve the quality of sentiment detection?
  • A. Focus only on complaint categorization and rely on post-processing to handle emotional nuance.
  • B. Include explicit context explaining the goal of sentiment analysis and define constraints for identifying urgency.
  • C. Remove detailed task instructions to give the Agent more freedom in interpreting customer messages.
  • D. Provide vague constraints in an emotional tone.
Answer: B
Explanation:
Ais correct - improving sentiment detection in agents begins with awell-structured promptthat includes explicit task contextand clearly defined expectations, especially when detecting nuanced emotions like frustration, urgency, or sarcasm.
According to UiPath'sPrompt Engineering Framework, a strong prompt should include:
* Atask objective: e.g., "Detect sentiment and urgency in user messages"
* Definitions or rules: e.g., "Urgency includes time sensitivity, threats of cancellation, or escalated language"
* Output constraints: e.g., "Classify as Positive, Neutral, Negative, and Urgent (Yes/No)" This helps the LLM:
* Anchor its reasoning to what urgency means inyour business context
* Avoid hallucinations or misinterpretation of neutral phrases
* Generateconsistently labeled outputsfor downstream automation or review Option B lacks structure - emotional tone # clarity.
C is risky - too much freedom leads to inconsistent results.
D separates tasks that arebest handled together, especially since emotion often influences how a complaint should be triaged.
Byembedding sentiment-specific logic into the prompt, UiPath agents become better equipped todetect critical issues in real time, enabling faster response and better customer experience.

NEW QUESTION # 34
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
  • A. Sigmoid similarity search
  • B. Cosine similarity search
  • C. ReLu similarity search
  • D. Softmax similarity search
Answer: B
Explanation:
Cis correct - UiPath'sContext Groundingsystem usescosine similarity searchto retrieve the most relevant documents, passages, or semantic chunks from indexed data during runtime.
This retrieval process is critical for:
* Minimizing hallucination
* Enhancingprompt grounding
* Connecting agents toreal-time enterprise knowledge
Here's how it works:
* All documents areembedded into vector spaceusing a model (e.g., OpenAI or Azure OpenAI embeddings)
* At runtime, the agent's query isconverted into a vector
* Acosine similaritycomparison is used to fetch themost semantically similar documentsbased onangle (not magnitude)between vectors This enables agents to provideaccurate, contextual answers, like referencing HR policies or IT guides.
Other options:
* A (Softmax) is a classifier, not a search method
* B (Sigmoid) is for binary classification
* D (ReLU) is an activation function in neural nets - not for search

NEW QUESTION # 35
When would it be most appropriate to use Web Search instead of Web Reader in an agent workflow?
  • A. When detailed, structured data is required from a known supplier's webpage.
  • B. When extracting time-sensitive data from a secure internal system.
  • C. When accessing and filtering information already embedded within a private enterprise knowledge base.
  • D. When the user needs a summarized overview from multiple public sources without a specific URL.
Answer: D
Explanation:
Cis correct - useWeb Searchin an agent workflow when you need the LLM toquery public internet sources(e.g., news, pricing, documentation), butdon't have a specific URL.
UiPath Autopilot and Agentic Agents distinguish:
* Web Search: For open-ended discovery from the web (e.g., "find latest refund policies from airlines")
* Web Reader: For extracting or summarizing content from aspecific, known URLor internal portal Web Search is ideal for:
* Aggregating public info
* Real-time summaries
* Context retrieval for grounding the prompt
A and B involveinternal sources- use tools likeKnowledge RetrievalorAPI connectorsinstead.
D calls fortargeted extraction, better suited toWeb Readerwith structured parsing.

NEW QUESTION # 36
What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?
  • A. To define static rules for retrieving data from the index.
  • B. To keep the ECS Index stored in a shared Orchestrator folder.
  • C. To retrieve data based on the user's current session or inputs.
  • D. To limit the number of results retrieved from the ECS Index.
Answer: C
Explanation:
Dis correct - the primary purpose of usingContext Grounding with an ECS (Enterprise Context Service) Indexin UiPath'sAgents designer canvasis to enablereal-time, dynamic retrieval of knowledgebased on the current user session or prompt inputs.
ECS indexes are built from documents, FAQs, policies, tickets, or any enterprise content and are used to:
* Provide agents withlive knowledge grounding
* Reduce hallucinations in LLM outputs
* Support tasks like Q&A, decision-making, and summarization
When a user inputs a query, the LLM canreference the ECS indexusing similarity search (usuallycosine similarity) to pullrelevant context chunksinto the prompt. This makes the agent smarter, safer, and more accurate.
Option A relates to deployment, not purpose.
B suggests hard-coded retrieval logic, which is the opposite of dynamic grounding.
C is about tuning, not the core purpose.
Context Grounding allows agents to actcontextually and intelligently, using up-to-date organizational data
- a foundational principle in UiPath's agentic architecture.

NEW QUESTION # 37
What is the defining characteristic of few-shot prompting?
  • A. It uses several examples to help the model understand the task better.
  • B. It requires the model to generate a response with no examples or instructions.
  • C. It links multiple prompts together in a sequential workflow.
  • D. It relies on intermediate reasoning steps to guide the model's response.
Answer: A
Explanation:
Dis correct - the defining feature offew-shot promptingis the inclusion ofmultiple input-output examples within the prompt todemonstrate the desired behavior or output structureto the LLM.
In UiPath's Agentic Prompting practices, few-shot examples help:
* Anchor the model to a consistent format
* Reduce ambiguity in task instructions
* Improve performance in tasks like classification, transformation, or content generation Example:
Input: "My password isn't working."
Output: "Category: Login Issue"
Input: "App won't open."
Output: "Category: Access Error"
This trains the model within the prompt - no fine-tuning required - making it apowerful design patternin building intelligent agents.
Option A describeschain-of-thought prompting.
B refers tozero-shot prompting.
C refers toprompt chaining, used in advanced orchestration, not few-shot logic.

NEW QUESTION # 38
......
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