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| Section | Objectives |
|---|---|
| Topic 1: Conversational Agents | - Conversational Agents |
| Topic 2: UI Agent | - UI Agent (ScreenPlay) |
| Topic 3: Coded Agents | - Coded Agents |
| Topic 4: Prompt engineering | - Designing structured few-shot prompts - Applying clarity, specificity, and conciseness - Crafting zero-shot prompts for simple tasks - Iteratively refining prompts based on evaluation feedback - Prompt engineering best practices - Including context, instructions, output formats, and constraints |
| Topic 5: UiPath Platform components and integrations | - Mapping connector actions into agent prompts - Troubleshooting tool execution using traces and evaluations - Creating agent tools in Studio Web - Data Fabric integration - Using Run Job in Studio Web - Creating connections through Integration Service - Integrating MCP assets as agent tools - Designing input and output schemas for tools - Applying agent tool guardrails - Reusing existing agents within new agents - Building and publishing API workflows as reusable agent tools |
| Topic 6: Context Grounding | - Ingestion and retrieval methods - Implementing index update strategies - Configuring thresholds and result limits for precision and relevance - Context grounding concepts and implementation - Monitoring index queries and jobs - Context grounding sources and configuration options - Using context grounding indexes to store and retrieve relevant information for agent memory - Context engineering best practices |
| Topic 7: Agent management | - Assigning policies at tenant, group, and user levels - Best practices for building, publishing, and deploying agents - Monitoring agent usage, unit consumption, and incidents - Analyzing execution traces to understand agent behavior - Applying governance policies to enable, disable, and control agent functionality - Agent tool and context grounding simulations - Using agent scores to track performance |
| Topic 8: AI Trust Layer | - Personally identifiable information masking in transit - Using audit logs and usage summaries - Using a custom tenant as an LLM gateway |
| Topic 9: Agentic orchestration with UiPath Maestro | - Maestro best practices - Defining global, local, and task-specific process variables - Mapping input and output parameters between Maestro tasks and agents - Chaining multiple agents within a process - Configuring Service Tasks to invoke automation workflows from Orchestrator - Instance management - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Defining and using API workflows - Configuring User Tasks with Action Apps forms for escalations - Maestro control flow - Using BPMN to model business processes and assign tasks to robots, agents, and humans - Configuring Service Tasks to invoke agents using Start and wait for agent - Using the expression editor and scripts for data transformation and conditional routing - Handling asynchronous agent completion events and callbacks - Using Send and Receive tasks with Integration Service connectors and webhooks - Capturing user inputs and returning structured data to processes |
| Topic 10: Agentic evaluations | - Iteratively improving agent performance - Executing evaluation sets and analyzing accuracy and reliability - Selecting deterministic and model-graded assertions - Using execution traces to troubleshoot failed evaluations - Creating input-output pairs for diverse scenarios - Differences between deterministic and model-graded evaluations - Optimizing prompts using evaluation results - Identifying edge cases and failure scenarios - Interpreting evaluation scores - Selecting strict or flexible assertions based on use cases - Designing evaluation datasets that reflect real-world complexity - Evaluation concepts and their role in agent reliability - Analyzing evaluation failures to identify root causes and error patterns |
| Topic 11: Agentic AI and agentic automation concepts | - Best practices for agentic automation, including intelligent decision-making, adaptability, goal-oriented execution, and multi-task coordination - Definitions of agents and distinctions between generative AI and agentic AI - Agentic orchestration and collaboration between robots, agents, and humans |
| Topic 12: Escalations | - Defining escalation logic - Configuring UiPath Action Apps for human-in-the-loop escalations - Human-in-the-loop concepts and the importance of human oversight |
| Topic 13: Autopilot for Everyone | - Using specialized Autopilots - Determining when to use Autopilot for Everyone |
| Topic 14: Agentic discovery and blueprint design | - Mapping business process steps to agent tasks - Differentiating system prompts and user prompts - Mapping context sources and grounding strategies - Defining agent goals and evaluation criteria - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Prioritizing automation opportunities based on feasibility and impact - Assessing agentic automation opportunities in business processes - Selecting tools appropriate to agent objectives |
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NEW QUESTION # 81
An automation developer needs an agent to extract the invoice number from incoming emails without being given any examples.
How should the zero-shot system prompt be written for this simple task?
Answer: D
Explanation:
Option A correctly applies zero-shot prompting to a narrowly defined information-extraction task.
Zero-shot prompting instructs a language model to perform a task without including demonstrations of the expected input-output relationship. This technique is appropriate when the task is sufficiently familiar and can be communicated through direct, unambiguous instructions.
In this scenario, the agent must read incoming email content, identify an invoice number, and return the extracted identifier when present.
A suitable instruction would define the source text, extraction objective, and expected output while avoiding unnecessary procedural complexity.
For production use, the prompt should also specify the behavior when no invoice number exists, such as returning a defined null value or an explicitly designated status. This prevents the agent from inventing identifiers when the required information is absent.
Option B describes few-shot prompting because it introduces example emails and expected outputs.
Option C adds tool and escalation complexity that is unnecessary for the stated extraction requirement. Option D increases instruction volume without necessarily improving reliability.
UiPath's prompting guidance emphasizes clear objectives, focused responsibilities, and explicit output requirements. These principles are especially important when an agent performs narrowly scoped extraction or classification operations.
Official UiPath references/topics: Agents - Prompts - System Prompt; Best Practices for Building Agents
- Scope Narrowly and Specialize.
NEW QUESTION # 82
During the automation potential identification phase, which combination of factors should be analyzed to properly understand process variations?
Answer: A
Explanation:
Option B represents the most comprehensive approach to evaluating process variations during automation discovery.
Before introducing agentic automation, an organization should understand how the existing business process operates across different scenarios and exception paths.
Each variation should be mapped according to the activities performed, applications involved, human intervention requirements, execution duration, and frequency of errors.
The systems used determine integration complexity and available automation mechanisms. Human touch points reveal where employees perform manual actions, interpret unstructured information, resolve exceptions, or make decisions.
Completion duration identifies delays and potential bottlenecks. Error rates help quantify process instability, rework, and opportunities for quality improvement.
This information allows automation architects to distinguish deterministic activities suitable for conventional RPA from judgment-intensive activities where AI agents can add value.
For example, a standard invoice approval may follow a predictable sequence, while invoices with missing purchase-order references may require contextual analysis and human escalation.
Option A omits performance and quality measurements. Option C assumes process variants are equivalent.
Option D concentrates on business outcomes without examining the operational causes.
A properly documented process baseline supports solution design, automation prioritization, and measurable comparisons between current-state and future-state performance.
Official UiPath references/topics: Agents - Best Practices for Building Agents; Process Mining - Process Analysis; Automation Hub - Opportunity Assessment.
NEW QUESTION # 83
What limitations apply when using Integration Service activities in UiPath agents, and how can files be handled in agent automations?
Answer: D
Explanation:
Option A most closely describes UiPath's Integration Service activity limitations and the recommended approach for file-processing operations.
UiPath Agents support Integration Service activities as tools, enabling governed API calls to enterprise applications through authenticated connections.
However, not every activity type is supported directly.
UiPath's October 2025 documentation specifies that API-based Integration Service activities are supported, while triggers and other non-API activity types are excluded.
Activities requiring mandatory file inputs or outputs are also excluded from direct agent activity-tool configuration. Activities containing optional file fields may remain available, but those specific file fields are omitted.
File-processing requirements can be addressed by invoking suitable RPA workflows as agent tools.
Current UiPath documentation additionally describes file-capable tools such as Analyze Files and Intelligent Xtraction and Processing (IXP), so file support is not limited exclusively to conventional RPA.
Option A requires an important correction concerning cascading fields. UiPath does not prohibit all cascading fields. It specifically prevents them from using the Prompt input method because dependent field structures cannot be dynamically reconstructed by the agent at runtime.
Options B and C incorrectly restrict agent integration capabilities, while option D ignores documented limitations.
Therefore, A is the intended answer, with the cascading-field qualification.
Official UiPath references/topics: Agents - October 2025 Release Notes - Expanded Integration Service Activities Support; Activities; Working with Files.
Technical qualification: Wording qualification: Cascading fields are not entirely unsupported; the Prompt input method is disallowed for cascading fields.
NEW QUESTION # 84
How can a UiPath process make sure that user input collected through the Properties Panel meets a specific format or set of constraints?
Answer: B
Explanation:
Option C correctly identifies input validation as the mechanism for enforcing specific requirements before user-supplied information is accepted.
UiPath Forms and Apps provide configurable input controls with validation properties that can enforce requirements independently of the descriptive text displayed to users.
Depending on the component, validation settings include mandatory-field requirements, minimum and maximum lengths, regular-expression patterns, and custom error messages.
For example, a form collecting a customer reference number may require a specific number of characters or a predefined alphanumeric pattern.
Configuring the appropriate validation rule allows the form to reject values that do not meet the specified requirements.
UiPath's Form Designer provides a Validation configuration section containing relevant controls, including Required, Minimum Length, Maximum Length, and Regular Expression Pattern.
Option A allows invalid information to enter the process before validation occurs. Option B provides a suggested value but does not restrict user input. Option D supplies instructions that users may disregard.
Although labels and default values improve usability, they are not substitutes for enforceable input constraints.
For critical business information, field-level validation should also be complemented by server-side or workflow-level validation before consequential transactions are executed.
Official UiPath references/topics: Activities - About Form Designer Controls; Apps - Textbox Validation Properties.
Technical qualification: The original question does not identify the precise form or application designer; controls are documented under component validation settings.
NEW QUESTION # 85
What is the primary risk of using strict assertions in creative or generative tasks?
Answer: C
Explanation:
Option D correctly identifies the principal limitation of strict assertions when evaluating generative AI responses.
Generative tasks frequently permit multiple acceptable outputs that communicate equivalent information using different wording, sentence structures, or formatting.
UiPath provides Deterministic Exact Match evaluators for requirements where actual and expected outputs must satisfy precise equality conditions.
These evaluators are appropriate for fixed values such as transaction identifiers, Boolean decisions, specific numbers, and other tightly constrained outputs.
However, applying Exact Match to creative or conversational responses may produce false failures.
For example, a customer-support agent could generate two explanations containing identical policy information but expressing that information in different sentences. An exact-match assertion might reject one response despite its substantive correctness.
UiPath's LLM-as-a-Judge Semantic Similarity evaluator offers a more appropriate alternative when the evaluation objective concerns meaning rather than identical wording.
Option A incorrectly assumes strict checks improve evaluation scores. Option B incorrectly suggests that assertions become unnecessary. Option C confuses strict validation with consistency of the underlying language-model output.
The correct approach is to match evaluation strictness to the business requirement.
Critical structured fields should receive deterministic checks, while natural-language responses should generally use semantic assessment supplemented by factual and policy-compliance controls.
Official UiPath references/topics: Agents - Evaluations, Deterministic Evaluators, LLM-as-a-Judge, and Choosing the Evaluator Type.
NEW QUESTION # 86
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