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| Section | Objectives |
|---|---|
| Topic 1: Coded Agents | - Coded Agents |
| Topic 2: UI Agent | - UI Agent (ScreenPlay) |
| Topic 3: Conversational Agents | - Conversational Agents |
| Topic 4: Escalations | - Configuring UiPath Action Apps for human-in-the-loop escalations - Defining escalation logic - Human-in-the-loop concepts and the importance of human oversight |
| Topic 5: Context Grounding | - Configuring thresholds and result limits for precision and relevance - Context grounding concepts and implementation - Context engineering best practices - 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 grounding sources and configuration options |
| Topic 6: Agentic discovery and blueprint design | - Selecting tools appropriate to agent objectives - Prioritizing automation opportunities based on feasibility and impact - Mapping business process steps to agent tasks - Mapping context sources and grounding strategies - 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 - Differentiating system prompts and user prompts |
| Topic 7: Autopilot for Everyone | - Determining when to use Autopilot for Everyone - Using specialized Autopilots |
| Topic 8: AI Trust Layer | - Personally identifiable information masking in transit - Using a custom tenant as an LLM gateway - Using audit logs and usage summaries |
| Topic 9: Agentic evaluations | - Selecting strict or flexible assertions based on use cases - Creating input-output pairs for diverse scenarios - Identifying edge cases and failure scenarios - Iteratively improving agent performance - Designing evaluation datasets that reflect real-world complexity - Interpreting evaluation scores - Differences between deterministic and model-graded evaluations - Analyzing evaluation failures to identify root causes and error patterns - Using execution traces to troubleshoot failed evaluations - Optimizing prompts using evaluation results - Selecting deterministic and model-graded assertions - Evaluation concepts and their role in agent reliability - Executing evaluation sets and analyzing accuracy and reliability |
| Topic 10: Prompt engineering | - Crafting zero-shot prompts for simple tasks - Iteratively refining prompts based on evaluation feedback - Including context, instructions, output formats, and constraints - Applying clarity, specificity, and conciseness - Designing structured few-shot prompts - Prompt engineering best practices |
| Topic 11: UiPath Platform components and integrations | - Reusing existing agents within new agents - Applying agent tool guardrails - Integrating MCP assets as agent tools - Mapping connector actions into agent prompts - Designing input and output schemas for tools - Data Fabric integration - Troubleshooting tool execution using traces and evaluations - Creating connections through Integration Service - Building and publishing API workflows as reusable agent tools - Creating agent tools in Studio Web - Using Run Job in Studio Web |
| Topic 12: Agentic AI and agentic automation concepts | - 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 - Agentic orchestration and collaboration between robots, agents, and humans |
| Topic 13: Agent management | - Applying governance policies to enable, disable, and control agent functionality - Best practices for building, publishing, and deploying agents - Analyzing execution traces to understand agent behavior - Assigning policies at tenant, group, and user levels - Using agent scores to track performance - Monitoring agent usage, unit consumption, and incidents - Agent tool and context grounding simulations |
| Topic 14: Agentic orchestration with UiPath Maestro | - Using the expression editor and scripts for data transformation and conditional routing - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Mapping input and output parameters between Maestro tasks and agents - Using Send and Receive tasks with Integration Service connectors and webhooks - Maestro best practices - 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 - Configuring User Tasks with Action Apps forms for escalations - Configuring Service Tasks to invoke automation workflows from Orchestrator - Maestro control flow - Defining and using API workflows - Handling asynchronous agent completion events and callbacks - Capturing user inputs and returning structured data to processes - Defining global, local, and task-specific process variables - Instance management - Chaining multiple agents within a process |
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NEW QUESTION # 76
During iterative optimization, which of the following changes is most likely to improve an agent's performance without adding latency?
Answer: D
Explanation:
Option D is the most appropriate optimization strategy because prompt refinement can improve decision quality without necessarily introducing additional execution stages.
The system prompt establishes the agent's objective, responsibilities, constraints, and instructions for using tools.
Ambiguous prompts may cause unnecessary reasoning, incorrect tool selection, repeated requests for information, or responses that do not satisfy business requirements.
Refining the prompt can reduce these problems by clarifying decision criteria, describing expected behavior, and defining precise output requirements.
For example, replacing a vague instruction to verify customer information with explicit validation conditions can help an agent select the correct verification tool and interpret its output consistently.
Unlike introducing additional tools or execution steps, a prompt-only improvement does not inherently require another external service call.
Option A increases potential tool-selection complexity. Option B changes the evaluation mechanism rather than the underlying agent behavior. Option C expands testing coverage without directly improving runtime execution.
UiPath's Optimize capability analyzes evaluation results and proposes targeted prompt modifications that developers can review before applying.
However, the phrase without adding latency requires qualification. A longer prompt or more extensive generated response can increase token processing time. Latency must therefore be measured rather than assumed unchanged.
Official UiPath references/topics: Agents - Evaluations - Optimizing Your Agent Through Evaluations; Prompt Editor.
Technical qualification: Latency qualification: Prompt refinement does not guarantee unchanged latency; token processing may increase.
NEW QUESTION # 77
When configuring evaluation sets to compare model/temperature combinations, which evaluator categories let you select the model used for evaluation?
Answer: A
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 # 78
Which practice best helps you iteratively improve an agent's prompt quality through UiPath evaluations?
Answer: A
Explanation:
Option C describes a disciplined iterative development process based on observed execution failures and repeatable validation.
UiPath evaluations allow developers to measure whether an agent produces expected results under representative input conditions.
When a debug run fails, the developer should inspect the execution trace, identify the reason for the incorrect behavior, and determine whether the system prompt requires modification.
Prompt refinements may clarify the agent's responsibilities, constraints, tool-selection criteria, exception handling, or expected output format.
After correcting the behavior, the developer should add the original failing scenario to an evaluation set with the appropriate expected result.
This establishes a regression test that can be executed during subsequent changes.
UiPath supports creating evaluations directly from debug runs and manually correcting expected outputs before adding them to an evaluation set.
Option A introduces unnecessary output variability. Option B changes evaluation criteria without correcting the underlying agent behavior. Option D manipulates aggregate scores by excluding difficult cases, producing an unreliable assessment of production readiness.
A robust improvement cycle includes representative test scenarios, stable evaluation criteria, trace analysis, prompt refinement, and repeated execution.
UiPath's Optimize functionality can also analyze evaluation results and propose prompt changes, but proposed changes should be reviewed and validated against the full evaluation set.
Official UiPath references/topics: Agents - Evaluations - Creating Evaluations from Test Runs; Optimizing Your Agent Through Evaluations.
NEW QUESTION # 79
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 # 80
Which statement best distinguishes deterministic evaluators from LLM-based (model-graded) evaluators when designing agent evaluations?
Answer: B
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 # 81
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