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| Section | Weight | Objectives |
|---|---|---|
| Agentic Discovery and Blueprint Design | 16% | - Tool selection and evaluation criteria definition - Process assessment and agent opportunity identification - Prompt engineering and context specification - Agent story design and goal definition |
| Context Grounding and Escalations | 16% | - Human-in-the-loop escalation architecture - Grounding source configuration and indexing - Action Apps and escalation SLA/routing design - Context engineering best practices - Retrieval strategies and ingestion management |
| Orchestration and Multi-Agent Processes | 17% | - UiPath Maestro process modeling and BPMN - API integration and external system connectivity - Human tasks, callbacks, and event handling - Multi-agent coordination and workflow control |
| Governance, Observability, and Reliability | 17% | - Guardrails, fallback logic, and risk controls - Compliance, security, and responsible AI - Governance policies and AI Trust Layer - Monitoring, observability, and Agent Scores |
| Advanced Agent Implementation and Platform Integration | 18% | - Studio Web tools and Integration Service configuration - UI Agent and ScreenPlay implementation - Coded Agents and conversational agents development - Autopilot and AI-assisted development workflows |
| Agentic Evaluations and Agent Management | 16% | - Incident analysis and iterative improvement - Deterministic vs model-graded evaluation - Agent lifecycle management and deployment - Assertions, scoring, and trace analysis - Evaluation dataset design and edge-case handling |
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NEW QUESTION # 74
During the automation potential identification phase, which combination of factors should be analyzed to properly understand process variations?
Answer: A
NEW QUESTION # 75
Which of the following is a properly structured system prompt based on best practices?
Original exhibit - Question 6, part 1
Original exhibit - Question 6, part 2
Answer: A
Explanation:
Option D identifies prompt example 4 as the most appropriate system prompt because it establishes a persistent operational identity, defines responsibilities, and provides instructions that govern the agent's behavior.
UiPath distinguishes between system prompts and user prompts. The system prompt establishes the agent's role, objectives, constraints, reasoning approach, and rules for interacting with tools or business context. The user prompt supplies task-specific information and runtime arguments.
Example 4 defines the agent as an AI assistant specializing in invoice and purchase-order discrepancies. It then provides explicit responsibilities covering document comparison, discrepancy identification, abbreviation handling, detailed analysis, and resolution recommendations.
The example also identifies the data attributes requiring examination, including quantities, pricing, dates, vendor information, purchase-order references, and additional charges. Its concluding goal establishes the required professional response characteristics.
Example 1 primarily provides instructions for analyzing a particular email and includes dynamic email content, making it more suitable for a task-specific prompt.
Example 2 provides minimal contextual information and lacks persistent behavioral instructions. Example 3 structures invoice input data but does not adequately establish the agent's responsibilities or decision-making requirements.
Example 4 therefore most closely satisfies UiPath's recommended system prompt structure.
Official UiPath references/topics: Agents - Prompts - System Prompt, User Prompt and Writing Effective Prompts.
NEW QUESTION # 76
You want to send an email within a BPMN.
Which Action should you select inside the task to allow you to send the email?
Answer: C
Explanation:
Option B identifies the correct Maestro implementation action for sending an email through an Integration Service connector.
UiPath Maestro uses Business Process Model and Notation (BPMN) to coordinate automation activities, enterprise applications, AI agents, and human tasks.
Within a BPMN process, a Send task can communicate with an external application using the Execute connector activity implementation.
This action invokes an Integration Service activity, such as a supported Microsoft Outlook or Gmail email- sending operation.
The developer selects the connector, configures the authenticated connection, chooses the required activity, and maps process variables to the activity's input fields.
Typical email inputs include the recipient address, subject, and message body. Relevant output values can also be mapped to process variables when supported by the activity.
Option A executes custom scripts and is not the standard connector-based approach. Option C waits for an external event rather than actively sending an email. Option D creates a human task through Action Center and does not itself perform the requested email operation.
The Execute connector activity option allows the BPMN process to interact with external systems while preserving centralized orchestration and execution traceability.
Official UiPath references/topics: Maestro Orchestrate - Send Task and Execute Connector Activity.
NEW QUESTION # 77
An agent needs to extract high-confidence data from structured documents and optionally permit human review before continuing.
Which tool configuration meets this need?
Answer: C
Explanation:
Option C precisely describes the documented integration between UiPath Intelligent Xtraction and Processing (IXP) and agent escalations.
UiPath Agents can use published IXP models as tools to extract structured information from business documents.
An IXP project defines the extraction taxonomy, including the fields and field groups that the model identifies. When an agent invokes the IXP tool, the extracted information becomes available for subsequent reasoning or tool operations.
Execution traces can also expose the extraction schema, input attachments, and confidence scores associated with predicted fields.
For scenarios requiring human verification, UiPath provides the Document Validation Station interface through an escalation configured with the Document Validation Task type.
The developer links the escalation to the appropriate IXP tool instance and configures a storage bucket for validation results.
A human reviewer can examine extracted values, review confidence information, and correct inaccurate fields. The validated information is subsequently returned to the agent so processing can continue.
Option A incorrectly treats DeepRAG as a document-extraction correction tool. Option B uses web retrieval instead of document extraction. Option D incorrectly associates human validation decisions with token limits.
Human validation should be triggered according to defined confidence, business-risk, or exception criteria, rather than assuming that every high-confidence extraction is necessarily correct.
Official UiPath references/topics: Agents - IXP: Adding an IXP Project as a Tool and Validation Station Escalation.
NEW QUESTION # 78
An organization is assessing a proposed agent story to determine whether it meaningfully addresses operational challenges.
Which evaluation criterion aligns most directly with agentic discovery principles?
Answer: B
Explanation:
Option C establishes the correct relationship between current-state process analysis and the justification for introducing agentic automation.
The "as-is" analysis documents how an existing business process operates, including its activities, decision points, systems, exceptions, manual interventions, and operational inefficiencies.
An agent story should address identifiable problems revealed by this analysis rather than introducing AI capabilities without a clearly defined business requirement.
For example, a process assessment may identify that employees spend substantial time interpreting inconsistent customer requests before routing them to the correct department.
This finding could justify introducing an agent responsible for contextual classification while preserving deterministic routing and downstream execution.
The proposed agent should therefore be connected to a documented pain point, a clearly defined responsibility, and measurable expected improvements.
Options A and D focus on increasing automation scope without establishing business value. Option B incorrectly treats additional architectural complexity as an objective.
UiPath's workflow-mapping guidance recommends identifying judgment-intensive steps, ambiguous inputs, and decision points before selecting agentic components.
This approach supports a defensible business case and reduces the risk of applying autonomous reasoning to processes better handled by conventional automation.
The evaluation should ultimately determine whether the proposed agent improves operational outcomes while maintaining appropriate reliability, governance, and maintainability.
Official UiPath references/topics: Agents - Workflow Mapping and Design Principles; Cartographer - AS- IS Process Documentation.
NEW QUESTION # 79
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