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| Section | Objectives |
|---|---|
| Prompt engineering | - Iteratively refining prompts based on evaluation feedback - Designing structured few-shot prompts - Crafting zero-shot prompts for simple tasks - Prompt engineering best practices - Applying clarity, specificity, and conciseness - Including context, instructions, output formats, and constraints |
| Agentic evaluations | - Evaluation concepts and their role in agent reliability - Selecting strict or flexible assertions based on use cases - Creating input-output pairs for diverse scenarios - Using execution traces to troubleshoot failed evaluations - Executing evaluation sets and analyzing accuracy and reliability - Interpreting evaluation scores - Iteratively improving agent performance - Differences between deterministic and model-graded evaluations - Identifying edge cases and failure scenarios - Optimizing prompts using evaluation results - Analyzing evaluation failures to identify root causes and error patterns - Designing evaluation datasets that reflect real-world complexity - Selecting deterministic and model-graded assertions |
| UiPath Platform components and integrations | - Building and publishing API workflows as reusable agent tools - Troubleshooting tool execution using traces and evaluations - Data Fabric integration - Mapping connector actions into agent prompts - Designing input and output schemas for tools - Using Run Job in Studio Web - Applying agent tool guardrails - Creating connections through Integration Service - Reusing existing agents within new agents - Integrating MCP assets as agent tools - Creating agent tools in Studio Web |
| Agentic orchestration with UiPath Maestro | - Instance management - Configuring Service Tasks to invoke agents using Start and wait for agent - Capturing user inputs and returning structured data to processes - Using Send and Receive tasks with Integration Service connectors and webhooks - Chaining multiple agents within a process - Defining global, local, and task-specific process variables - Handling asynchronous agent completion events and callbacks - Maestro best practices - Maestro control flow - Using the expression editor and scripts for data transformation and conditional routing - Configuring User Tasks with Action Apps forms for escalations - Defining and using API workflows - Using BPMN to model business processes and assign tasks to robots, agents, and humans - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Mapping input and output parameters between Maestro tasks and agents - Configuring Service Tasks to invoke automation workflows from Orchestrator |
| Context Grounding | - Context grounding concepts and implementation - Implementing index update strategies - Using context grounding indexes to store and retrieve relevant information for agent memory - Ingestion and retrieval methods - Monitoring index queries and jobs - Context engineering best practices - Configuring thresholds and result limits for precision and relevance - Context grounding sources and configuration options |
| Agentic discovery and blueprint design | - Selecting tools appropriate to agent objectives - Mapping context sources and grounding strategies - Assessing agentic automation opportunities in business processes - Prioritizing automation opportunities based on feasibility and impact - Mapping business process steps to agent tasks - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Differentiating system prompts and user prompts - Defining agent goals and evaluation criteria |
| Coded Agents | - Coded Agents |
| UI Agent | - UI Agent (ScreenPlay) |
| 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 |
| Autopilot for Everyone | - Determining when to use Autopilot for Everyone - Using specialized Autopilots |
| Agent management | - Agent tool and context grounding simulations - Using agent scores to track performance - Best practices for building, publishing, and deploying agents - Assigning policies at tenant, group, and user levels - Analyzing execution traces to understand agent behavior - Monitoring agent usage, unit consumption, and incidents - Applying governance policies to enable, disable, and control agent functionality |
| Conversational Agents | - Conversational Agents |
| AI Trust Layer | - Personally identifiable information masking in transit - Using a custom tenant as an LLM gateway - Using audit logs and usage summaries |
| Agentic AI and agentic automation concepts | - Agentic orchestration and collaboration between robots, agents, and humans - Definitions of agents and distinctions between generative AI and agentic AI - Best practices for agentic automation, including intelligent decision-making, adaptability, goal-oriented execution, and multi-task coordination |
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NEW QUESTION # 77
What is the main design goal when chaining multiple agents in one process for complex automations?
Answer: C
Explanation:
Option D correctly identifies the importance of explicit coordination boundaries when multiple agents participate in a complex business process.
UiPath supports agent composition by allowing published agents to be invoked as tools and by providing orchestration capabilities for coordinating agents, robotic workflows, enterprise integrations, and human activities.
Each agent should perform a clearly defined responsibility and produce structured outputs that downstream components can interpret reliably.
Handoff rules specify the circumstances in which responsibility transfers from one component to another.
For example, an invoice classification agent may pass a validated classification to a financial verification agent. The downstream agent should receive the required data through a defined input schema and return a structured result.
Where classification uncertainty is relevant, the solution can use an appropriately validated confidence threshold or business-rule condition to determine whether processing continues or requires additional review.
Option A incorrectly assumes final human review guarantees correctness. Option B undermines the benefits of specialized agents. Option C permits uncontrolled delegation without safeguards.
For predictable orchestration, architects should also define error handling, timeout behavior, retries, escalation routes, and transaction boundaries.
UiPath recommends explicit tool descriptions and clear invocation conditions so agents understand when delegation is appropriate.
Official UiPath references/topics: Agents - Agents as Tools; Building Effective Agent Tools; Prompts, including tool invocation and escalation instructions.
NEW QUESTION # 78
According to the UiPath Agents documentation, where can you view the evaluation score (0-100) and detailed results after running an evaluation set to assess accuracy and reliability?
Answer: D
Explanation:
Option D identifies the documented location for reviewing the outcome of an evaluation set in UiPath Agents.
The evaluation framework allows developers to compare an agent's actual outputs and execution behavior against predefined expectations.
After configuring representative evaluation cases, the developer executes the evaluation set using the Evaluate set action.
UiPath then presents the resulting scores and detailed evaluation outcomes in the Results tab.
These results help determine whether the agent satisfies the configured assertions and provide evidence for comparing changes made during development.
The score referenced in the question uses a 0-100 scale to represent evaluation performance according to the applicable assessment criteria.
Option A confuses evaluation-set results with the separate Agent Score feature used in earlier versions of the agent development experience.
Option B identifies the Execution Trail, which displays operational details such as tool invocations, inputs, outputs, and execution errors. Although these details support troubleshooting, they are not the primary evaluation-results location.
Option C concerns Orchestrator execution monitoring rather than the evaluation workspace.
A reliable development process should inspect both numerical evaluation results and detailed traces. High aggregate scores alone do not establish that every critical business scenario has been handled correctly.
Official UiPath references/topics: Agents - Evaluations, Working with Evaluation Sets.
NEW QUESTION # 79
During evaluation, an agent responsible for extracting signatures and related information from tables in approval documents produces inconsistent results and misses some signatures.
Based on iterative refinement practices, what corrective action should be taken?
Answer: B
Explanation:
Option B applies targeted prompt refinement and measurable evaluation to the specific extraction defect identified during testing.
UiPath recommends systematically improving agent prompts by adjusting relevant instructions, executing representative tests, and comparing the resulting performance against established criteria.
The scenario identifies a narrowly defined problem: signatures embedded within approval-document tables are sometimes omitted.
Rather than rewriting the entire prompt, the developer should clarify how the agent must inspect tabular content, identify signature fields, associate signatures with the correct approvers, and handle partially populated rows or unusual table structures.
These additional instructions should address the observed failure without introducing unrelated requirements.
After modifying the prompt, the developer should rerun the affected evaluation cases and measure extraction performance.
Relevant metrics may include signature recall, field-level accuracy, missed-signature counts, and the frequency of incorrectly associated signatories.
Option A does not directly modify the defective extraction behavior. Option C introduces unnecessary broad changes, making regression analysis more difficult. Option D modifies input arguments even though the scenario does not establish an argument-schema defect.
A production implementation should also assess whether specialized document-processing capabilities such as UiPath IXP would improve extraction reliability.
Prompt refinement remains the correct immediate response to the described instruction-level issue.
Official UiPath references/topics: Agents - Prompts, Iterations; Best Practices for Building Agents.
NEW QUESTION # 80
An agent has several tools configured, but during testing it takes actions unrelated to its defined objective.
What adjustment would most effectively improve alignment with that objective?
Answer: A
Explanation:
Option A applies the principle of least privilege to agent tool configuration and directly limits the actions available to the agent.
UiPath Agents execute business operations through explicitly configured tools, including Integration Service activities, published automations, other agents, and MCP capabilities.
Each additional tool expands the range of operations the agent can potentially select during execution. Tools that are unrelated to the agent's objective introduce unnecessary decision complexity and may increase the risk of unintended actions.
When an agent performs activities outside its defined responsibilities, removing unnecessary tools establishes an enforceable capability boundary.
For example, an agent responsible for determining invoice payment status may require read-only invoice retrieval and validation tools. Providing invoice deletion, supplier-account modification, or unrestricted payment-processing tools creates unnecessary operational exposure.
Refining the system prompt can improve tool-selection decisions, but prompt instructions alone do not prevent an agent from invoking an available capability incorrectly.
Option B retains excessive privileges. Option C expands the available capabilities further. Option D clarifies the objective without restricting execution permissions.
An appropriate implementation combines focused tools, well-defined descriptions, least-privilege credentials, and guardrails. Evaluation sets should then verify that the agent selects only authorized tools and avoids unrelated actions.
Official UiPath references/topics: Agents - Tools; Building Effective Agent Tools; MCP Servers Compliance.
NEW QUESTION # 81
When configuring evaluation sets to compare model/temperature combinations, which evaluator categories let you select the model used for evaluation?
Answer: C
Explanation:
Option B correctly identifies the evaluator categories that expose a Model selection field in UiPath Agent evaluations.
UiPath separates evaluators into three principal categories: Deterministic, LLM-as-a-Judge, and Trajectory.
Deterministic evaluators use predefined comparison rules to evaluate outputs. Exact Match and JSON Similarity are examples. Because these evaluators do not require language-model inference to calculate their results, they do not include a Model selection field.
LLM-as-a-Judge evaluators use a language model to assess response characteristics such as semantic similarity, faithfulness, and contextual relevance. Their configuration allows developers to select the evaluation model.
Trajectory evaluators also use an LLM, but their purpose is to examine the agent's overall execution behavior, including intermediate actions, decisions, and tool usage.
Consequently, both LLM-as-a-Judge and Trajectory evaluators support model selection.
Option A incorrectly excludes LLM-as-a-Judge evaluators. Option C incorrectly assigns model selection to deterministic evaluators. Option D ignores the distinction between model-based and rule-based scoring.
Importantly, choosing an evaluator's model is different from choosing the model and temperature used to execute the agent. These are separate configuration concerns.
Official UiPath references/topics: Agents - Evaluations: Evaluator Categories and Defining Evaluators.
NEW QUESTION # 82
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