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| Section | Objectives |
|---|---|
| Context Grounding and Knowledge Retrieval | - Knowledge sources and retrieval configuration
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| Agentic Automation Fundamentals and Solution Design | - Agentic automation concepts, use cases, and architecture
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| Agentic Orchestration with UiPath Maestro | - Process orchestration and workflow coordination
|
| AI Agents and Prompt Engineering | - Agent behavior, instructions, and tool usage
|
| Agent Evaluation and Quality Assurance | - Evaluation strategies and test datasets
|
| Governance, Security, and Responsible AI | - Enterprise governance and risk management
|
| Human Oversight and User Interaction | - Human-in-the-loop controls
|
| Integration, Reliability, and Operations | - Enterprise integration and production readiness
|
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NEW QUESTION # 46
When configuring the Search Strategy for a Context in UiPath agents, which parameter lets you control how many chunks are returned from a user's query?
Answer: D
Explanation:
Option C identifies the specific Context Grounding setting that controls the maximum number of retrieved chunks returned for an agent query.
UiPath Context Grounding allows agents to retrieve relevant content from indexed enterprise documents.
When the Semantic search strategy is selected, developers can configure parameters that influence retrieval precision and result quantity.
The Max results generated setting determines how many chunks Context Grounding returns in response to the query.
Increasing this value allows more retrieved passages to be included in the grounding payload. This may improve coverage when information is distributed across several document sections.
However, returning additional chunks also increases the amount of context supplied to the language model, potentially raising token consumption and introducing irrelevant information.
Reducing the value limits the retrieved content and can improve efficiency when the most relevant information appears within a small number of chunks.
Option B describes the Relevance score threshold, which filters retrieved chunks according to their similarity scores rather than defining the maximum result count.
Option A is not the documented parameter. Option D concerns the language model's broader context capacity rather than Context Grounding's retrieval-result limit.
UiPath recommends calibrating the result count together with the relevance threshold to balance retrieval completeness, precision, and token efficiency.
Official UiPath references/topics: Agents - Contexts, Semantic Search Strategy, Max Results Generated, and Relevance Score Threshold.
NEW QUESTION # 47
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 # 48
During a design-time Debug test run, an agent produces an incorrect output for a realistic failure scenario. The team wants to capture this scenario in the dataset with the expected outcome so it can be re-run after fixes.
According to the documentation, what is an appropriate way to create this evaluation?
Answer: B
Explanation:
Option C accurately describes UiPath's documented method for converting an unsuccessful design-time execution into a reusable evaluation case.
UiPath Agents support creating evaluation cases directly from Debug test runs. This allows developers to capture realistic scenarios encountered during agent development instead of relying exclusively on manually invented examples.
When a Debug execution produces an incorrect result, the developer can generate an evaluation from that execution and modify the expected output to reflect the correct business outcome.
This preserves the original input and execution scenario while establishing an appropriate assertion for subsequent regression testing.
For example, an agent may incorrectly classify a customer refund request as a general support inquiry. The developer can retain the original customer message while replacing the incorrect classification with the expected refund-request category.
After refining the prompt, tool configuration, or relevant context, the evaluation can be rerun to determine whether the defect has been corrected.
Option A confuses design-time evaluation creation with runtime trace feedback. Option B incorrectly treats the failed output as the target result. Option D applies an evaluator without correcting the expected outcome.
The critical principle is that evaluation datasets must represent intended behavior, not merely reproduce whatever the agent previously generated.
Official UiPath references/topics: Agents - Evaluations, Creating Evaluations from Test Runs.
NEW QUESTION # 49
In the context of automation, what key capability distinguishes generative AI (GenAI) from agentic AI?
Answer: C
Explanation:
Option B accurately identifies the distinction between content generation and autonomous, goal-directed execution.
Generative AI primarily creates new content by processing prompts and generating responses such as text, images, software code, summaries, and other information artifacts.
Agentic AI extends these capabilities by incorporating contextual perception, reasoning, planning, tool invocation, and action execution to pursue defined objectives.
UiPath describes agentic AI as technology that enables autonomous agents to make decisions, coordinate activities, and adapt their actions according to changing circumstances.
For example, a generative AI application may draft an email recommending adjustments to a delivery schedule. An agentic automation solution can analyze delivery constraints, determine appropriate changes, invoke scheduling tools, update operational records, and initiate notifications.
This distinction is especially relevant within UiPath's automation architecture, where agents can combine language-model reasoning with robotic process automation, Integration Service activities, APIs, and orchestration components.
However, agentic autonomy does not eliminate governance requirements. Enterprise deployments still need defined permissions, traceability, exception handling, and human oversight when appropriate.
Options A, C, and D reverse or misrepresent the respective capabilities of generative and agentic AI.
Official UiPath references/topics: UiPath - What Is Agentic AI?; What Is Agentic Automation?; Agents and Workflows.
NEW QUESTION # 50
A tool must behave consistently by using the same value for a specific input field whenever the agent runs.
Which input method design accomplishes this?
Answer: C
Explanation:
Option B correctly identifies the Static Value input method as the appropriate configuration when a tool parameter must remain constant across executions.
UiPath Agents support three input methods for Integration Service activity tools: Prompt, Argument, and Static Value.
The Prompt method allows the language model to infer the appropriate value dynamically using the available context and tool description.
Argument retrieves the field value from a predefined agent input argument, allowing upstream workflows or triggering systems to supply information at runtime.
Static Value assigns a fixed configuration value directly to the activity field. The agent uses that value whenever the activity executes, provided the tool configuration remains unchanged.
For example, an agent may invoke a ticket-creation activity that must always assign a particular category, business unit, or predefined operating mode. Configuring the relevant field as Static Value prevents the model from substituting a different value.
Option A relies on probabilistic inference rather than explicit configuration. Option C incorrectly treats guardrails as a mechanism for supplying fixed values. Option D misrepresents input arguments, which receive values from the agent's input configuration rather than being regenerated from tool outputs.
Static Value therefore provides predictable parameter assignment and reduces unnecessary runtime variability.
Official UiPath references/topics: Agents - Activities: Defining Activity Input Methods.
NEW QUESTION # 51
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