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| Section | Objectives |
|---|---|
| Escalations | - Human-in-the-loop concepts and the importance of human oversight - Defining escalation logic - Configuring UiPath Action Apps for human-in-the-loop escalations |
| Agentic discovery and blueprint design | - Mapping business process steps to agent tasks - Assessing agentic automation opportunities in business processes - Prioritizing automation opportunities based on feasibility and impact - Differentiating system prompts and user prompts - Mapping context sources and grounding strategies - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Selecting tools appropriate to agent objectives - Defining agent goals and evaluation criteria |
| UiPath Platform components and integrations | - Applying agent tool guardrails - Creating connections through Integration Service - Data Fabric integration - Using Run Job in Studio Web - Mapping connector actions into agent prompts - Building and publishing API workflows as reusable agent tools - Troubleshooting tool execution using traces and evaluations - Designing input and output schemas for tools - Integrating MCP assets as agent tools - Creating agent tools in Studio Web - Reusing existing agents within new agents |
| Agentic evaluations | - Analyzing evaluation failures to identify root causes and error patterns - Using execution traces to troubleshoot failed evaluations - Selecting deterministic and model-graded assertions - Identifying edge cases and failure scenarios - Executing evaluation sets and analyzing accuracy and reliability - Designing evaluation datasets that reflect real-world complexity - Differences between deterministic and model-graded evaluations - Iteratively improving agent performance - Interpreting evaluation scores - Evaluation concepts and their role in agent reliability - Optimizing prompts using evaluation results - Selecting strict or flexible assertions based on use cases - Creating input-output pairs for diverse scenarios |
| Prompt engineering | - Iteratively refining prompts based on evaluation feedback - Applying clarity, specificity, and conciseness - Including context, instructions, output formats, and constraints - Prompt engineering best practices - Designing structured few-shot prompts - Crafting zero-shot prompts for simple tasks |
| AI Trust Layer | - Using audit logs and usage summaries - Using a custom tenant as an LLM gateway - Personally identifiable information masking in transit |
| Autopilot for Everyone | - Using specialized Autopilots - Determining when to use Autopilot for Everyone |
| Agentic orchestration with UiPath Maestro | - Configuring User Tasks with Action Apps forms for escalations - Maestro control flow - Defining and using API workflows - Defining global, local, and task-specific process variables - Capturing user inputs and returning structured data to processes - Using the expression editor and scripts for data transformation and conditional routing - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Handling asynchronous agent completion events and callbacks - Maestro best practices - Instance management - Using BPMN to model business processes and assign tasks to robots, agents, and humans - Configuring Service Tasks to invoke automation workflows from Orchestrator - Configuring Service Tasks to invoke agents using Start and wait for agent - Using Send and Receive tasks with Integration Service connectors and webhooks - Chaining multiple agents within a process - Mapping input and output parameters between Maestro tasks and agents |
| Agent management | - Monitoring agent usage, unit consumption, and incidents - Assigning policies at tenant, group, and user levels - Analyzing execution traces to understand agent behavior - Agent tool and context grounding simulations - Using agent scores to track performance - Applying governance policies to enable, disable, and control agent functionality - Best practices for building, publishing, and deploying agents |
| 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 |
| Conversational Agents | - Conversational Agents |
| Context Grounding | - Context engineering best practices - Configuring thresholds and result limits for precision and relevance - Using context grounding indexes to store and retrieve relevant information for agent memory - Context grounding concepts and implementation - Monitoring index queries and jobs - Context grounding sources and configuration options - Ingestion and retrieval methods - Implementing index update strategies |
| Coded Agents | - Coded Agents |
| UI Agent | - UI Agent (ScreenPlay) |
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NEW QUESTION # 103
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 # 104
Which statement best distinguishes deterministic evaluators from LLM-based (model-graded) evaluators when designing agent evaluations?
Answer: A
Explanation:
Option A correctly identifies the operational characteristics of deterministic evaluators within UiPath's agent evaluation framework.
Deterministic evaluators calculate results using predefined comparison rules rather than language-model interpretation. Consequently, identical evaluation inputs produce consistent results when the evaluator configuration remains unchanged.
UiPath provides deterministic evaluation types such as Exact Match and JSON Similarity. Exact Match is appropriate when the output must satisfy a precise expected value, including Boolean results, numerical values, specific strings, and arrays containing primitive values.
Because deterministic evaluators do not invoke an LLM to judge results, their configuration does not require a Model selection field.
LLM-as-a-Judge evaluators use language models to assess responses according to semantic similarity, contextual relevance, faithfulness, or other defined criteria. These evaluators are more suitable for natural- language responses where different wording may still represent an acceptable result.
Trajectory evaluators assess broader execution behavior, including intermediate decisions and tool usage, rather than restricting evaluation to final-output equality.
Options B and C incorrectly assign probabilistic or model-based characteristics to deterministic evaluators.
Option D describes trajectory-oriented evaluation.
The correct selection depends on whether the validation objective requires strict equality or flexible assessment.
Official UiPath references/topics: Agents - Evaluations - Evaluator Categories and Choosing the Evaluator Type.
NEW QUESTION # 105
What is the primary risk of using strict assertions in creative or generative tasks?
Answer: B
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 # 106
What is an appropriate strategy for handling ambiguous inputs in an agentic orchestration design?
Answer: B
Explanation:
Option C provides the most appropriate approach for resolving ambiguous information while maintaining control over agentic workflow execution.
Agentic orchestration combines autonomous reasoning, deterministic robotic automation, enterprise-system integrations, and human decision-making.
When an input cannot be interpreted reliably, the process should identify the uncertainty and determine the appropriate exception-handling route.
For example, an invoice-processing agent may encounter an invoice referencing multiple purchase orders or an ambiguous supplier identifier. Automatically selecting a default value could result in incorrect financial processing.
Instead, the orchestration should categorize the exception and route the relevant information to an authorized human reviewer.
UiPath Maestro supports coordinating human tasks and automated activities within a BPMN-based process.
UiPath Agents can also invoke escalations through Action Apps when human approval or clarification is required.
After the reviewer resolves the ambiguity, the result should be returned to the workflow so processing can continue along the appropriate route.
Resolved cases can also inform future improvements to prompts, evaluation datasets, routing conditions, or business rules. Such improvements require an intentional feedback mechanism; they are not automatic.
Options A and D create unacceptable processing outcomes. Option B provides human intervention but omits the feedback mechanism necessary for systematic improvement.
Official UiPath references/topics: Agents - Escalations; Maestro Orchestrate - Human Tasks; Agents - Evaluations.
NEW QUESTION # 107
How can an agent with Escalations be evaluated?
Answer: D
Explanation:
Option C is the intended selection because escalation simulation is a recommended method of evaluating agent behavior without requiring live human intervention.
UiPath Agents use escalations to request approval, validation, or assistance from human reviewers through Action Apps and Action Center.
During evaluation, an agent may encounter a scenario requiring an escalation. Allowing the actual escalation to execute can create a human task and introduce external dependencies into the test.
UiPath supports simulation of escalation behavior so developers can supply predefined or generated responses without waiting for a reviewer.
A simulated response might represent approval, rejection, or a request for additional information. The developer can then examine whether the agent continues correctly after receiving that result.
Simulation improves repeatability, reduces operational side effects, and supports automated regression testing.
However, current UiPath documentation permits both real and simulated executions within evaluation sets.
Runtime evaluations can invoke live escalations when those components are not simulated.
Consequently, option C is not universally correct as written. Option A incorrectly states that escalations cannot execute, option B excludes automated evaluation, and option D incorrectly assumes that mocking occurs automatically.
The accurate recommendation is to simulate escalations when controlled, side-effect-free evaluation is required.
Official UiPath references/topics: Agents - Evaluations - Configuring Simulations; Managing Runtime Evaluations.
Technical qualification: Inaccurate wording: C is the intended answer, but escalation simulation is optional, not universally mandatory.
NEW QUESTION # 108
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