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| Section | Objectives |
|---|---|
| Escalations | - Defining escalation logic - Human-in-the-loop concepts and the importance of human oversight - Configuring UiPath Action Apps for human-in-the-loop escalations |
| Agentic orchestration with UiPath Maestro | - Using BPMN to model business processes and assign tasks to robots, agents, and humans - 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 - Using Send and Receive tasks with Integration Service connectors and webhooks - Defining global, local, and task-specific process variables - Configuring User Tasks with Action Apps forms for escalations - Handling asynchronous agent completion events and callbacks - Configuring Service Tasks to invoke agents using Start and wait for agent - Using the expression editor and scripts for data transformation and conditional routing - Maestro control flow - Instance management - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Capturing user inputs and returning structured data to processes - Defining and using API workflows - Maestro best practices |
| Agent management | - Using agent scores to track performance - Agent tool and context grounding simulations - Analyzing execution traces to understand agent behavior - Assigning policies at tenant, group, and user levels - Best practices for building, publishing, and deploying agents - Monitoring agent usage, unit consumption, and incidents - Applying governance policies to enable, disable, and control agent functionality |
| Conversational Agents | - Conversational Agents |
| Agentic evaluations | - Differences between deterministic and model-graded evaluations - Identifying edge cases and failure scenarios - Optimizing prompts using evaluation results - Iteratively improving agent performance - Evaluation concepts and their role in agent reliability - Selecting strict or flexible assertions based on use cases - Analyzing evaluation failures to identify root causes and error patterns - Interpreting evaluation scores - Selecting deterministic and model-graded assertions - Executing evaluation sets and analyzing accuracy and reliability - Using execution traces to troubleshoot failed evaluations - Creating input-output pairs for diverse scenarios - Designing evaluation datasets that reflect real-world complexity |
| AI Trust Layer | - Using a custom tenant as an LLM gateway - Using audit logs and usage summaries - Personally identifiable information masking in transit |
| Context Grounding | - Ingestion and retrieval methods - Monitoring index queries and jobs - Using context grounding indexes to store and retrieve relevant information for agent memory - Implementing index update strategies - Context grounding concepts and implementation - Context grounding sources and configuration options - Context engineering best practices - Configuring thresholds and result limits for precision and relevance |
| Autopilot for Everyone | - Using specialized Autopilots - Determining when to use Autopilot for Everyone |
| Prompt engineering | - Crafting zero-shot prompts for simple tasks - Including context, instructions, output formats, and constraints - Iteratively refining prompts based on evaluation feedback - Applying clarity, specificity, and conciseness - Prompt engineering best practices - Designing structured few-shot prompts |
| Agentic discovery and blueprint design | - Prioritizing automation opportunities based on feasibility and impact - Selecting tools appropriate to agent objectives - Mapping context sources and grounding strategies - Differentiating system prompts and user prompts - Defining agent goals and evaluation criteria - Assessing agentic automation opportunities in business processes - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Mapping business process steps to agent tasks |
| UiPath Platform components and integrations | - Building and publishing API workflows as reusable agent tools - Reusing existing agents within new agents - Designing input and output schemas for tools - Data Fabric integration - Integrating MCP assets as agent tools - Mapping connector actions into agent prompts - Creating connections through Integration Service - Troubleshooting tool execution using traces and evaluations - Using Run Job in Studio Web - Applying agent tool guardrails - Creating agent tools in Studio Web |
| Agentic AI and agentic automation concepts | - Agentic orchestration and collaboration between robots, agents, and humans - 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 |
| Coded Agents | - Coded Agents |
| UI Agent | - UI Agent (ScreenPlay) |
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NEW QUESTION # 67
A team wants an agent to be selectable in the Run Job activity within Studio Web workflows.
Which lifecycle step best makes it available?
Answer: A
Explanation:
Option C correctly identifies publishing as the lifecycle operation required to make a completed UiPath Agent available for orchestration and invocation from other automation workflows.
UiPath recommends testing, evaluating, and refining an agent before releasing it into a production environment.
During development, the agent exists within its design environment, where developers can debug prompts, evaluate responses, simulate tools, and inspect execution traces.
Publishing packages the agent and registers the published artifact within UiPath Orchestrator.
However, publishing alone does not complete the entire deployment lifecycle. The published solution must also be deployed before the agent becomes available as an executable resource.
Following deployment, supported Studio Web workflows can invoke the agent through the Run Job activity.
This enables developers to combine agent reasoning with conventional workflows and larger orchestration processes.
Option A refers to observing execution behavior rather than releasing the agent. Option B concerns simulated testing. Option D supports troubleshooting during development.
None of these operations substitutes for publishing and deployment.
The release process should also validate input/output arguments, runtime permissions, required connections, and dependencies to ensure successful invocation by downstream workflows.
Official UiPath references/topics: Agents - Publishing and Deploying the Agent - Publishing to Orchestrator.
NEW QUESTION # 68
What is an appropriate strategy for handling ambiguous inputs in an agentic orchestration design?
Answer: A
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 # 69
When should a UiPath developer create evaluations for an agent?
Answer: C
Explanation:
Option A directly matches UiPath's documented guidance regarding the appropriate stage for creating structured agent evaluations.
UiPath recommends establishing evaluation sets once the agent's input and output arguments are stable, the business use case is well defined, and the principal configuration components are sufficiently finalized.
These components include the system prompt, user prompt, available tools, and Context Grounding sources.
The reason for this recommendation is that evaluations depend on the agent's input schema, expected outputs, and operational behavior.
Changing argument definitions after creating evaluation cases may require corresponding modifications to evaluation inputs, assertions, and expected results.
Similarly, substantial changes to tool interfaces or context configuration can invalidate previously established test expectations.
Option B proposes constructing formal evaluations before the agent's behavior or interfaces are defined. Early exploratory testing is useful, but complete evaluation datasets are more maintainable after the principal design is stable.
Options C and D incorrectly postpone validation until production.
UiPath encourages developers to create representative evaluation sets before deployment, then expand them as new scenarios and runtime evidence become available.
The resulting evaluations support regression detection, configuration comparisons, and systematic improvement throughout the agent lifecycle.
Official UiPath references/topics: Agents - Evaluations: When to Create Evaluations.
NEW QUESTION # 70
An organization is designing a customer-facing service agent using the agent story blueprint format.
To align with UiPath agent components, which story structure best reflects proper instruction and interaction design?
Answer: B
Explanation:
Option B provides the most complete agent story structure because it aligns with UiPath's principal agent design components and operational recommendations.
An agent story should begin with a defined role and operating environment, establishing what the agent is responsible for and the business context in which it operates.
The prompt should specify the agent's objectives, behavioral instructions, constraints, task sequence, and expected output structure.
Context identifies the information sources required for informed decisions. UiPath Context Grounding can provide access to relevant enterprise documents and knowledge repositories.
Tools define how the agent interacts with external systems, executes automations, or retrieves operational information.
Measurable success criteria establish how performance will be evaluated, including accuracy, response consistency, completion rates, and adherence to business requirements.
Escalation paths identify circumstances requiring human validation, approval, or exception handling.
Option A omits essential execution and governance components. Option C lacks detailed agent instructions and relies on incomplete performance indicators. Option D focuses on administrative and analytical characteristics without sufficiently defining agent behavior.
A well-designed agent story connects responsibilities, instructions, knowledge, execution capabilities, performance measurements, and human oversight into a coherent operational design.
Official UiPath references/topics: Agents - Best Practices for Building Agents; Escalations; Contexts.
NEW QUESTION # 71
Which monitoring setup is appropriate for proactively tracking index updates and catching errors early?
Answer: B
Explanation:
Option D identifies the documented event-based notification mechanism for monitoring UiPath Context Grounding ingestion operations.
Context Grounding indexes must remain synchronized with their underlying knowledge sources to provide agents with relevant and sufficiently current information.
Ingestion jobs perform the operations necessary to process source content and update the indexed knowledge base.
UiPath supports notifications associated with three ingestion events: Ingestion Job Started, Ingestion Job Completed, and Ingestion Job Failed.
Administrators can subscribe to these notifications through the platform's notification management interface.
The Started event provides visibility into the beginning of an ingestion operation. The Completed event indicates successful completion, while the Failed event alerts administrators to problems requiring investigation.
UiPath also supports notification subscriptions based on event severity, including Success and Error.
Option A incorrectly associates an invented alert with individual retrieval operations. Option B assumes a specific scheduled tenant-health email containing ingestion information. Option C confuses an index usage attribute with an automatic event-based alerting mechanism.
For production deployments, administrators should particularly prioritize failure notifications and investigate recurring ingestion errors that could leave agents using outdated knowledge.
Event monitoring should complement scheduled synchronization, index health checks, retrieval testing, and ongoing evaluation of grounded response quality.
Official UiPath references/topics: Automation Cloud - Working with Context Grounding, Notifications.
NEW QUESTION # 72
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