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| Section | Objectives |
|---|---|
| Agentic AI and agentic automation concepts | - Best practices for agentic automation, including intelligent decision-making, adaptability, goal-oriented execution, and multi-task coordination - Agentic orchestration and collaboration between robots, agents, and humans - Definitions of agents and distinctions between generative AI and agentic AI |
| Agentic orchestration with UiPath Maestro | - Maestro control flow - Maestro best practices - Capturing user inputs and returning structured data to processes - Configuring Service Tasks to invoke automation workflows from Orchestrator - Defining and using API workflows - Configuring Service Tasks to invoke agents using Start and wait for agent - Defining global, local, and task-specific process variables - Configuring User Tasks with Action Apps forms for escalations - Instance management - Chaining multiple agents within a process - Using the expression editor and scripts for data transformation and conditional routing - Mapping input and output parameters between Maestro tasks and agents - Handling asynchronous agent completion events and callbacks - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Using Send and Receive tasks with Integration Service connectors and webhooks - Using BPMN to model business processes and assign tasks to robots, agents, and humans |
| Context Grounding | - Context grounding sources and configuration options - Context grounding concepts and implementation - Using context grounding indexes to store and retrieve relevant information for agent memory - Monitoring index queries and jobs - Ingestion and retrieval methods - Configuring thresholds and result limits for precision and relevance - Context engineering best practices - Implementing index update strategies |
| UI Agent | - UI Agent (ScreenPlay) |
| Agentic evaluations | - Selecting strict or flexible assertions based on use cases - Optimizing prompts using evaluation results - Designing evaluation datasets that reflect real-world complexity - Executing evaluation sets and analyzing accuracy and reliability - Analyzing evaluation failures to identify root causes and error patterns - Differences between deterministic and model-graded evaluations - Using execution traces to troubleshoot failed evaluations - Selecting deterministic and model-graded assertions - Interpreting evaluation scores - Evaluation concepts and their role in agent reliability - Creating input-output pairs for diverse scenarios - Identifying edge cases and failure scenarios - Iteratively improving agent performance |
| Agent management | - Applying governance policies to enable, disable, and control agent functionality - Monitoring agent usage, unit consumption, and incidents - Best practices for building, publishing, and deploying agents - Agent tool and context grounding simulations - Analyzing execution traces to understand agent behavior - Using agent scores to track performance - Assigning policies at tenant, group, and user levels |
| AI Trust Layer | - Using a custom tenant as an LLM gateway - Personally identifiable information masking in transit - Using audit logs and usage summaries |
| Coded Agents | - Coded Agents |
| Agentic discovery and blueprint design | - Mapping business process steps to agent tasks - Prioritizing automation opportunities based on feasibility and impact - Assessing agentic automation opportunities in business processes - Mapping context sources and grounding strategies - Selecting tools appropriate to agent objectives - Defining agent goals and evaluation criteria - Differentiating system prompts and user prompts - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools |
| Autopilot for Everyone | - Determining when to use Autopilot for Everyone - Using specialized Autopilots |
| Prompt engineering | - Applying clarity, specificity, and conciseness - Crafting zero-shot prompts for simple tasks - Including context, instructions, output formats, and constraints - Iteratively refining prompts based on evaluation feedback - Designing structured few-shot prompts - Prompt engineering best practices |
| Conversational Agents | - Conversational Agents |
| UiPath Platform components and integrations | - Reusing existing agents within new agents - Troubleshooting tool execution using traces and evaluations - Building and publishing API workflows as reusable agent tools - Creating agent tools in Studio Web - Using Run Job in Studio Web - Mapping connector actions into agent prompts - Designing input and output schemas for tools - Data Fabric integration - Creating connections through Integration Service - Integrating MCP assets as agent tools - Applying agent tool guardrails |
| 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 |
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NEW QUESTION # 15
An insurance company needs to automate end-to-end policy validation by ingesting customer records from its CRM, invoking multiple REST APIs to verify coverage and eligibility against external data sources, and reconciling results for audit compliance. The process must handle authentication challenges, intermittent API outages, and changing response schemas, while providing clear progress and error reporting for business users.
Which workflow design approach is best?
Answer: B
Explanation:
Option A represents the most appropriate enterprise integration architecture because it addresses reliability, security, maintainability, and operational transparency together.
UiPath Integration Service provides a centralized mechanism for establishing authenticated connections with external applications. These connections can be reused across automation activities, reducing credential duplication and supporting controlled authentication lifecycle management. Where a suitable connector is unavailable, custom API integrations can implement equivalent authentication controls.
A modular architecture separates CRM ingestion, eligibility verification, response transformation, and reconciliation into independently maintainable components. Schema validation is particularly important because external APIs may introduce optional properties, modify response structures, or return unexpected payloads. Normalizing these responses before downstream processing reduces integration failures.
Resilience can be implemented through exception handling, bounded retries, and appropriate recovery procedures. UiPath's Retry Scope activity supports retrying eligible operations. However, retry behavior must consider API rate limits, timeouts, and transaction idempotency to prevent duplicate changes.
Centralized logging and transaction-level status reporting allow administrators and business stakeholders to identify failed records, investigate exceptions, and support audit requirements.
Options B, C, and D introduce avoidable operational risks through hardcoded credentials, excessive manual intervention, or incomplete exception handling.
Official UiPath references/topics: Integration Service - Connections; Authentication Configuration; System Activities - Retry Scope.
NEW QUESTION # 16
Which of the following accurately describes the recommended workflow mapping exercise to complete before building an agent?
Answer: D
Explanation:
Option B most closely represents UiPath's recommended workflow mapping exercise before agent development.
UiPath emphasizes that effective agents should be embedded within existing business processes rather than designed as unrestricted replacements for complete workflows.
The first responsibility during workflow mapping is identifying the process's decision points and input/output formats. These characteristics establish where business information enters the process, what decisions must be made, and which results downstream components require.
The next activity is identifying processes with clear business objectives but variable or ambiguous inputs.
Ticket classification, compliance assessment, and document interpretation are suitable examples when decisions require contextual reasoning.
Predictable, rules-based execution steps should generally remain in conventional RPA workflows or deterministic orchestration components. Agents should be introduced where contextual interpretation, judgment, or adaptive planning adds value.
Finally, the design should preserve the surrounding workflow architecture. The agent operates as a specialized decision-making component, while conventional automation handles repeatable execution and process control.
Options A, C, and D introduce activities such as user training, interface design, and deployment planning.
Although these can be necessary elsewhere in the project lifecycle, they are not the core workflow mapping activities specified by UiPath.
Official UiPath references/topics: Agents - Best Practices for Building Agents - Design Agents for Real Workflows.
Technical qualification: Wording qualification: UiPath emphasizes processes with variable or ambiguous inputs; the term "predictable flows" is imprecise.
NEW QUESTION # 17
When designing an agent to operate across changing environments and multiple objectives, which practice best balances control and adaptability?
Answer: A
Explanation:
Option B most appropriately combines goal-directed agent behavior with the flexibility required to operate under changing conditions.
UiPath differentiates autonomous agents from deterministic workflows partly through their capacity to interpret context, plan actions, and adapt execution behavior.
An effective agent design begins with explicit business objectives, measurable success criteria, and constraints defining permissible actions.
These requirements provide a stable operational framework even when individual tasks or environmental conditions change.
Within that framework, the agent can adjust its selection and sequencing of tools according to available information and previous execution results.
For example, a procurement agent may initially attempt supplier verification through an authorized API. If that service becomes temporarily unavailable, the agent might use another approved verification mechanism or escalate the exception, depending on its configured instructions.
Option A restricts adaptability by fixing dependencies regardless of operational conditions. Option C introduces unnecessary human intervention for ordinary adjustments. Option D prevents the agent from responding appropriately to meaningful contextual differences.
Adaptability must nevertheless remain bounded. Agents should not arbitrarily modify authorization rules, bypass mandatory approvals, or alter deterministic business controls.
UiPath's prompting guidance recommends clear objectives, planned execution steps, tool-use conditions, and escalation rules that allow agent behavior to evolve while maintaining governance.
Official UiPath references/topics: Agents - Prompts, Clear Goal and Objective; Best Practices for Building Agents; Agents and Workflows.
NEW QUESTION # 18
What must be configured to create an Automation Ops policy for a specific tenant?
Answer: D
Explanation:
Option C most closely matches the general policy-details configuration described in the official UiPath Automation Ops documentation.
Automation Ops provides centralized governance controls for supported UiPath products, including Studio, Integration Service, and AI Trust Layer capabilities.
When an administrator creates or edits a governance policy, the Policy Details section includes a policy name, priority, description, and an availability setting.
The policy name identifies the governance configuration and should be unique enough to distinguish it from other policies.
Priority determines precedence when multiple group-level policies apply to the same user. UiPath applies the policy with the lowest numerical priority value first.
Description provides supplementary information about the policy's purpose and intended application.
Option A incorrectly substitutes Roles for Description. Option B omits Priority. Option D incorrectly introduces Period in place of the documented Priority setting.
However, the original question is incomplete if interpreted as an exhaustive list of mandatory fields.
Current UiPath documentation also includes Availability (days), which determines how long a cached policy remains applicable when the governed product cannot connect to Automation Ops.
Furthermore, the description is optional, and creating a policy does not automatically apply it to a tenant. The policy must subsequently be deployed to the intended tenant.
Official UiPath references/topics: Automation Ops - Configure a Governance Policy; Deploy Governance Policies.
Technical qualification: C best matches principal policy details, but description is optional and Availability (days) is another configuration property.
NEW QUESTION # 19
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 # 20
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