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| Section | Objectives |
|---|---|
| Agentic discovery and blueprint design | - Prioritizing automation opportunities based on feasibility and impact - Differentiating system prompts and user prompts - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Assessing agentic automation opportunities in business processes - Mapping context sources and grounding strategies - Mapping business process steps to agent tasks - Defining agent goals and evaluation criteria - Selecting tools appropriate to agent 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 |
| AI Trust Layer | - Using audit logs and usage summaries - Personally identifiable information masking in transit - Using a custom tenant as an LLM gateway |
| Agentic orchestration with UiPath Maestro | - Using the expression editor and scripts for data transformation and conditional routing - Configuring Service Tasks to invoke automation workflows from Orchestrator - Defining global, local, and task-specific process variables - Defining and using API workflows - Maestro control flow - Using BPMN to model business processes and assign tasks to robots, agents, and humans - Instance management - Configuring Service Tasks to invoke agents using Start and wait for agent - Using Send and Receive tasks with Integration Service connectors and webhooks - Mapping input and output parameters between Maestro tasks and agents - Maestro best practices - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Chaining multiple agents within a process - Handling asynchronous agent completion events and callbacks - Capturing user inputs and returning structured data to processes - Configuring User Tasks with Action Apps forms for escalations |
| Agent management | - Analyzing execution traces to understand agent behavior - Best practices for building, publishing, and deploying agents - Using agent scores to track performance - Agent tool and context grounding simulations - Applying governance policies to enable, disable, and control agent functionality - Assigning policies at tenant, group, and user levels - Monitoring agent usage, unit consumption, and incidents |
| Coded Agents | - Coded Agents |
| Autopilot for Everyone | - Determining when to use Autopilot for Everyone - Using specialized Autopilots |
| Agentic evaluations | - Iteratively improving agent performance - Analyzing evaluation failures to identify root causes and error patterns - Interpreting evaluation scores - Optimizing prompts using evaluation results - Selecting deterministic and model-graded assertions - Differences between deterministic and model-graded evaluations - Evaluation concepts and their role in agent reliability - Using execution traces to troubleshoot failed evaluations - Designing evaluation datasets that reflect real-world complexity - Identifying edge cases and failure scenarios - Creating input-output pairs for diverse scenarios - Selecting strict or flexible assertions based on use cases - Executing evaluation sets and analyzing accuracy and reliability |
| UiPath Platform components and integrations | - Using Run Job in Studio Web - Troubleshooting tool execution using traces and evaluations - Creating agent tools in Studio Web - Designing input and output schemas for tools - Creating connections through Integration Service - Mapping connector actions into agent prompts - Reusing existing agents within new agents - Building and publishing API workflows as reusable agent tools - Data Fabric integration - Integrating MCP assets as agent tools - Applying agent tool guardrails |
| Prompt engineering | - Prompt engineering best practices - Designing structured few-shot prompts - Applying clarity, specificity, and conciseness - Iteratively refining prompts based on evaluation feedback - Including context, instructions, output formats, and constraints - Crafting zero-shot prompts for simple tasks |
| UI Agent | - UI Agent (ScreenPlay) |
| Conversational Agents | - Conversational Agents |
| Context Grounding | - Context engineering best practices - Using context grounding indexes to store and retrieve relevant information for agent memory - Context grounding sources and configuration options - Monitoring index queries and jobs - Implementing index update strategies - Configuring thresholds and result limits for precision and relevance - Context grounding concepts and implementation - Ingestion and retrieval methods |
| 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 |
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NEW QUESTION # 43
An agent consistently struggles to generate accurate responses despite having a well-structured prompt.
Which evaluation strategy would best isolate whether the problem lies in prompt clarity or tool invocation logic?
Answer: D
Explanation:
Option C is the strongest listed choice because LLM-as-a-Judge evaluation can determine whether generated responses satisfy the semantic intent of the agent's instructions.
UiPath's Semantic Similarity evaluator compares actual and expected responses according to meaning rather than requiring identical text.
This helps identify cases where the agent produces fluent responses that do not satisfy the intended business objective.
However, semantic evaluation alone cannot conclusively distinguish poor prompt interpretation from incorrect tool invocation.
For example, an agent may misunderstand instructions and select the wrong tool. Alternatively, it may select the correct tool but supply invalid arguments or misinterpret the returned result.
Both situations can produce low semantic evaluation scores.
Therefore, a rigorous investigation should combine output-oriented evaluation with execution traces and tool- call evaluators.
UiPath supports inspecting recorded tool arguments, outputs, execution order, and associated errors.
Runtime evaluation capabilities also include Tool Call Arguments, Tool Call Count, Tool Call Order, and Tool Call Output evaluators.
Options A and D rely on deterministic output comparison without directly identifying behavioral causes.
Option B introduces artificial execution variability rather than isolating the defect.
Option C is the intended answer, but complete diagnostic isolation requires additional trajectory and tool- execution evidence.
Official UiPath references/topics: Agents - Evaluations - Semantic Similarity; Managing Runtime Evaluations - Tool Call Evaluators; Agent Traces.
Technical qualification: Diagnostic qualification: Semantic similarity alone cannot isolate prompt issues from tool invocation problems; traces and tool-call assertions are also necessary.
NEW QUESTION # 44
An evaluation repeatedly fails when the agent calls a particular tool, but the summary shows no explicit error.
Which action would provide the clearest diagnostic insight?
Answer: A
Explanation:
Option C identifies execution-trace inspection as the most reliable method for diagnosing an agent evaluation failure associated with a particular tool.
UiPath's agent tracing functionality records execution details beyond the final result or evaluation score.
A trace can identify the tool selected by the agent, the arguments supplied during invocation, the response returned by the tool, and its position within the overall execution sequence.
These details help distinguish several possible failure conditions.
The agent may have selected the correct tool but supplied invalid arguments. Alternatively, the tool may have executed successfully but returned information that the agent interpreted incorrectly.
Another possibility is that the agent invoked the tool before retrieving required contextual information or completing a prerequisite operation.
Examining the recorded inputs, outputs, and sequence allows the developer to determine which condition caused the failed evaluation.
Option A replaces a component before establishing the failure mechanism. Option B expands test coverage but does not directly explain the failure. Option D changes the business requirement to avoid the problematic behavior.
UiPath also provides tool-call evaluators for validating invocation arguments, call counts, execution order, and returned outputs. These can supplement trace inspection when recurring defects must be detected automatically.
Official UiPath references/topics: Agents - Agent Traces; Managing Runtime Evaluations - Tool Call Evaluators.
NEW QUESTION # 45
An internal audit team creates a prompt requiring an agent to determine whether control descriptions are Adequate or Inadequate. The prompt contains the agent's role, evaluation criteria, two labeled control examples, and a new description.
What is the primary advantage of this few-shot structured prompt?
Answer: D
Explanation:
Option B correctly identifies the benefit of combining explicit evaluation criteria with representative labeled examples in a few-shot prompt.
Few-shot prompting provides a language model with a small number of examples demonstrating how a task should be performed.
For an internal audit classification task, the agent must distinguish control descriptions that satisfy defined requirements from those that do not.
The prompt should first establish the agent's role and the criteria used to classify a control as Adequate or Inadequate.
Labeled examples then demonstrate how those criteria apply to representative control descriptions.
For example, an adequate control may specify a responsible owner, execution frequency, evidence requirements, and a clearly defined verification procedure.
An inadequate control might omit accountability, provide vague operating instructions, or fail to establish measurable compliance requirements.
The examples help the agent recognize the expected classification behavior when evaluating a new description.
Option A incorrectly suggests that examples eliminate the need for explicit success criteria. Option C would permit inconsistent modification of audit requirements. Option D overlooks the instructional value of the labeled examples.
UiPath recommends including representative input-output examples while avoiding excessive examples that unnecessarily increase prompt size.
A reliable implementation should also evaluate borderline descriptions and ensure that classification criteria remain consistent across executions.
Official UiPath references/topics: Agents - Prompts, Writing Effective Prompts; Best Practices for Building Agents.
NEW QUESTION # 46
An automation candidate has high improvement potential, but root cause analysis shows that the issue stems from inconsistent upstream data quality.
How should this influence prioritization?
Answer: D
Explanation:
Option A represents the appropriate response when automation discovery identifies underlying operational problems that automation alone may not resolve.
UiPath Process Mining enables organizations to investigate process variations, identify inefficiencies, and analyze factors contributing to undesirable business outcomes.
Automation Hub additionally supports assessing automation feasibility, potential, implementation effort, and expected business benefits.
A high improvement-potential estimate does not automatically establish that a process is technically suitable for automation.
When root cause analysis identifies inconsistent upstream data quality, the proposed automation may inherit the same defects.
For example, an invoice reconciliation process might experience frequent exceptions because supplier identifiers are inconsistent across source applications.
Introducing an agent to reconcile those invoices may reduce some manual effort, but it will not necessarily eliminate the underlying data-quality problem.
The organization should therefore reassess the candidate's implementation feasibility, expected accuracy, exception frequency, and dependency on upstream remediation.
Option B omits essential validation. Option C incorrectly treats estimated potential as sufficient justification.
Option D prioritizes automation-rate improvement over the actual business problem.
An appropriate discovery outcome may involve correcting source-data validation, standardizing input schemas, or implementing upstream controls before deploying the agent.
The final prioritization decision should consider both expected business value and the cost of addressing the root cause.
Official UiPath references/topics: Process Mining - Root Cause Analysis; Automation Hub - Detailed Assessment Algorithm; Process Mining - Simulating Automation Potential.
NEW QUESTION # 47
A team wants to improve an agent's reliability while keeping its prompt efficient and focused.
Which iteration strategy best follows effective prompt design principles?
Answer: A
Explanation:
Option C directly reflects UiPath's recommended approach to iterative prompt engineering.
Effective agent prompts should provide sufficient instructions for reliable execution without introducing unnecessary complexity, contradictory requirements, or excessive context.
UiPath recommends systematically varying prompt components, including role instructions, task decomposition, reasoning approaches, output formatting, and contextual information.
Each modification should be evaluated against representative test scenarios.
Developers should record the effect of changes on measurable outcomes such as response accuracy, completeness, constraint adherence, and task success.
For example, an invoice-processing agent may repeatedly misclassify partially paid invoices. Instead of rewriting its entire prompt, the developer can introduce a specific payment-status interpretation rule and evaluate the resulting behavior.
If the revision improves performance across relevant scenarios without causing regressions, it can be retained.
Option A encourages uncontrolled prompt expansion. Option B removes structure required for reliable execution. Option D prevents meaningful experimentation and relies on subjective output assessment.
The objective is not necessarily the shortest possible prompt, but the minimal collection of instructions that consistently produces the required behavior.
Evaluation sets provide the evidence needed to determine which instructions contribute to reliability and which introduce unnecessary complexity.
Official UiPath references/topics: Agents - Prompts: Writing Effective Prompts and Iterations; Best Practices for Building Agents.
NEW QUESTION # 48
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