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| Section | Objectives |
|---|---|
| Topic 1: Escalations & Human-in-the-Loop | - Exception handling and escalation patterns - Action Center workflows |
| Topic 2: Agentic Orchestration (Maestro) | - BPMN-based process design - Workflow orchestration with agents |
| Topic 3: Agentic AI Fundamentals | - AI agents vs rule-based automation - Agentic automation concepts |
| Topic 4: Prompt Engineering for Agents | - System prompts and constraints - Zero-shot and few-shot prompting |
| Topic 5: Context Grounding (RAG) | - Data grounding strategies - Retrieval-Augmented Generation concepts |
| Topic 6: Agent Discovery & Process Assessment | - Identifying automation opportunities - Process suitability for agentic automation |
| Topic 7: Agent Blueprint Design | - Agent architecture design - Workflow decomposition |
| Topic 8: Autopilot for Everyone | - AI-assisted automation building - Use cases and capabilities |
| Topic 9: UiPath Platform Components | - Agent Builder and Orchestrator basics - Studio Web and Autopilot |
| Topic 10: Agentic Evaluations & Governance | - Guardrails and validation logic - LLM-as-a-judge metrics |
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NEW QUESTION # 39
Which persona typically models agentic processes in Maestro with BPMN and governs their full lifecycle?
Answer: A
Explanation:
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.
NEW QUESTION # 40
What is a characteristic of using Business Process Model and Notation by process excellence practitioners?
Answer: C
Explanation:
The correct answer isC-Business Process Model and Notation (BPMN)is astandards-based modeling languageused byprocess excellence practitionersto visually define, communicate, and govern business workflows.
In UiPath'sMaestroorchestration platform, BPMN acts as acollaborative bridgebetween:
* Business stakeholders(who define processes and goals)
* Technical implementers(who build automations and agent logic)
BPMN includes rich constructs such as:
* Gateways for conditional logic
* Events for escalations and errors
* Tasks, subprocesses, and human interventions
This makes itideal for dynamic, agentic workflows- not just static process mapping.
A and B are false - BPMN is built foradaptive,automated, andcollaborativeorchestration.
D is wrong - BPMN supportserror handling, retries, and fallback flows, all critical in agentic automation.
NEW QUESTION # 41
In which scenario is a deterministic evaluation more appropriate than a model-graded one?
Answer: C
Explanation:
Cis correct -deterministic evaluationsare best suited for cases where thecorrect output is known and fixed
, allowing for binary or rule-based validation.
Examples include:
* Exact matches (e.g., status: "Approved")
* Regex pattern checks
* Structured JSON outputs
* Correct field extraction (e.g., invoice number = INV-2023-0021)
UiPath supportsdeterministic evaluationusing logic like:
* "Output equals Expected"
* "Contains X and Y"
* "JSON schema is valid"
This is distinct frommodel-graded evaluations, which are used when outputs areopen-endedorqualitative(e.
g., summarization, sentiment, tone). These require LLM-based grading to assess whether the output is "good enough" even if it varies slightly.
Option A and B refer tosubjective assessmentsbetter suited formodel-graded scoring.
D implies feedback-driven quality, again requiringflexible interpretation, not deterministic checking.
Deterministic methods offerspeed, clarity, and automationin validation - ideal for tasks where there'sonly one right answer.
NEW QUESTION # 42
You are building an agent that classifies incoming emails into one of three categories: Urgent, Normal, or Spam. You want to improve accuracy by using few-shot examples in a structured format. Which approach best supports this goal?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (from UiPath Agentic Automation documentation):
The correct approach isC, as it best reflects thefew-shot prompting pattern, which is a well-documented and recommended technique in both UiPath Autopilot™ and broader agentic AI design for improvingintent classificationaccuracy.
InUiPath Agentic Automation, especially inPrompt Engineering, few-shot examples serve to "ground" the Large Language Model (LLM) with task-specific context. Providingstructured input-output pairs(as shown in option C) allows the model to learn from the context and mirror the expected output more reliably - enhancing classification precision.
For instance, UiPath recommends using clearly formatted training examples in this structure:
Input: "[Text]"
Output: "[Label]"
This aligns with UiPath's guidance under thePrompt Engineering Framework, which highlights that using few-shot exemplars with clear task demonstrationsignificantly improves model performance over zero- shot or ambiguous input formats (as in options A or B). Option D also underperforms due to insufficient grounding.
UiPath emphasizes the importance oflabel clarity,format consistency, andexplicit instruction- all of which are satisfied in Option C. This method also supportspromptgeneralizationfor new inputs by modeling how categorization should happen, not just what categories exist.
This technique is crucial in real-world agentic workflows where LLMs handle noisy, unstructured data (like emails), and are expected to trigger appropriate downstream actions such as ticket creation, escalation, or filtering.
NEW QUESTION # 43
When you want a connector field value to be inferred dynamically at run time, which input method should you select in the activity tool?
Answer: B
Explanation:
The correct answer isD- selecting"Argument"allows a field value in an activity (such as a connector or tool call) to bedynamically inferred at runtime, based on variables, agent state, or previous node outputs.
UiPath Autopilot™ and Studio Web use the"Argument"option inactivity configurationto passdynamic values, especially in agentic workflows where:
* Outputs of one step must inform inputs of the next
* Contextual reasoning or prompt outputs need to feed tool parameters
* Escalation decisions or classifications affect API calls or record updates This is fundamental in making agent behavioradaptive and responsive to user context- a key trait of UiPath's agentic orchestration layer.
Other options:
* A (Static value) is hardcoded
* B (Clear value) wipes any existing input
* C (Prompt) is used when engaging the LLM, not connectors
NEW QUESTION # 44
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