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| Section | Weight | Objectives |
|---|---|---|
| Governance, Evaluation & Trust | 15% | - Guardrails and responsible AI - Observability and reliability - Agent performance evaluation |
| Orchestration & Human-in-the-Loop | 20% | - UiPath Maestro and BPMN modeling - Escalations and Action Center - Service tasks and agent invocation |
| Agent Design & Development | 25% | - Agent Builder in Studio Web - Prompt engineering best practices - Tools, connections and integration services |
| Agentic Automation Fundamentals | 20% | - Agents vs traditional robots - AI, LLM and generative AI concepts - Core principles of agentic automation |
| Context & Knowledge Management | 20% | - Context packages and grounding - Knowledge base integration - Data sources and retrieval methods |
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NEW QUESTION # 35
What is a System Prompt?
Answer: D
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 # 36
How does agentic orchestration ensure consistency and reliability in processes?
Answer: B
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 # 37
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?
Answer: D
Explanation:
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.
NEW QUESTION # 38
When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?
Answer: C
Explanation:
Dis correct - UiPathContext Groundingsupports querying unstructured and semi-structured data, including CSV files, by embedding their content intosemantic representationssuch asJSON-formatted chunksduring indexing.
Here's how this works for CSVs:
* UiPathparses the tabular dataand maps each row or section into asemantically rich format(e.g., JSON)
* These JSON-structured embeddings are then stored inECS Indexes(Enterprise Context Store)
* When an LLM agent queries the index, it retrieves themost contextually relevant data, even across large datasets This unlocks:
* Smarter question answeringfrom tabular data
* Cross-referencing multiple fieldsin a single query
* Enhanced LLM understanding by transforming flat rows intorelational, structured prompts Option A is misleading - LLMs rely onsemantic similarity, not SQL-like structured queries.
B is false - CSV is not auto-converted into XLSX.
C is incorrect - streaming is not yet supported; indexing is a prerequisite.
In short, UiPath enablessemantic grounding of structured datalike CSVs by reformatting them intoJSON- style embeddings, improving retrieval quality, summarization, and task-specific use cases.
NEW QUESTION # 39
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?
Answer: D
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 # 40
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