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| Section | Objectives |
|---|---|
| Topic 1: AI Trust Layer | - Personally identifiable information masking in transit - Using audit logs and usage summaries - Using a custom tenant as an LLM gateway |
| Topic 2: Coded Agents | - Coded Agents |
| Topic 3: UI Agent | - UI Agent (ScreenPlay) |
| Topic 4: Autopilot for Everyone | - Determining when to use Autopilot for Everyone - Using specialized Autopilots |
| Topic 5: 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 |
| Topic 6: Agentic AI and agentic automation concepts | - Definitions of agents and distinctions between generative AI and agentic AI - Agentic orchestration and collaboration between robots, agents, and humans - Best practices for agentic automation, including intelligent decision-making, adaptability, goal-oriented execution, and multi-task coordination |
| Topic 7: Prompt engineering | - Prompt engineering best practices - Iteratively refining prompts based on evaluation feedback - Designing structured few-shot prompts - Applying clarity, specificity, and conciseness - Including context, instructions, output formats, and constraints - Crafting zero-shot prompts for simple tasks |
| Topic 8: Agent management | - Assigning policies at tenant, group, and user levels - Best practices for building, publishing, and deploying agents - Analyzing execution traces to understand agent behavior - Monitoring agent usage, unit consumption, and incidents - Using agent scores to track performance - Applying governance policies to enable, disable, and control agent functionality - Agent tool and context grounding simulations |
| Topic 9: UiPath Platform components and integrations | - Applying agent tool guardrails - Building and publishing API workflows as reusable agent tools - Reusing existing agents within new agents - Designing input and output schemas for tools - Mapping connector actions into agent prompts - Data Fabric integration - Using Run Job in Studio Web - Troubleshooting tool execution using traces and evaluations - Creating agent tools in Studio Web - Creating connections through Integration Service - Integrating MCP assets as agent tools |
| Topic 10: Agentic discovery and blueprint design | - Prioritizing automation opportunities based on feasibility and impact - Designing agent stories using triggers, instructions, context, human-in-the-loop requirements, and tools - Defining agent goals and evaluation criteria - Mapping context sources and grounding strategies - Assessing agentic automation opportunities in business processes - Differentiating system prompts and user prompts - Mapping business process steps to agent tasks - Selecting tools appropriate to agent objectives |
| Topic 11: Conversational Agents | - Conversational Agents |
| Topic 12: Agentic orchestration with UiPath Maestro | - Configuring dynamic assignees, SLAs, and expiry rules for user tasks - Configuring Service Tasks to invoke agents using Start and wait for agent - Defining and using API workflows - Maestro best practices - Instance management - Using Send and Receive tasks with Integration Service connectors and webhooks - Handling asynchronous agent completion events and callbacks - Configuring Service Tasks to invoke automation workflows from Orchestrator - Mapping input and output parameters between Maestro tasks and agents - Defining global, local, and task-specific process variables - Configuring User Tasks with Action Apps forms for escalations - Using the expression editor and scripts for data transformation and conditional routing - Using BPMN to model business processes and assign tasks to robots, agents, and humans - Maestro control flow - Chaining multiple agents within a process - Capturing user inputs and returning structured data to processes |
| Topic 13: Agentic evaluations | - Interpreting evaluation scores - Iteratively improving agent performance - Selecting deterministic and model-graded assertions - Analyzing evaluation failures to identify root causes and error patterns - Optimizing prompts using evaluation results - Using execution traces to troubleshoot failed evaluations - Selecting strict or flexible assertions based on use cases - Identifying edge cases and failure scenarios - Differences between deterministic and model-graded evaluations - Evaluation concepts and their role in agent reliability - Designing evaluation datasets that reflect real-world complexity - Creating input-output pairs for diverse scenarios - Executing evaluation sets and analyzing accuracy and reliability |
| Topic 14: Context Grounding | - Context grounding sources and configuration options - Context engineering best practices - Monitoring index queries and jobs - Configuring thresholds and result limits for precision and relevance - Ingestion and retrieval methods - Using context grounding indexes to store and retrieve relevant information for agent memory - Context grounding concepts and implementation - Implementing index update strategies |
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NEW QUESTION # 43
Which search strategy should you select when your knowledge base contains CSV-formatted tabular data?
Answer: D
Explanation:
Option B correctly identifies the Structured search strategy for querying CSV-formatted tabular information through UiPath Context Grounding.
Context Grounding supports several retrieval strategies intended for different data structures and information- processing requirements.
Semantic search retrieves relevant content using similarity-based matching. It is particularly useful for unstructured documents and natural-language knowledge retrieval.
Structured search is designed for tabular datasets where information is organized into rows and columns.
UiPath explicitly documents CSV support for the Structured search strategy.
For example, an agent may need to retrieve records from a CSV containing customer identifiers, invoice amounts, payment statuses, and transaction dates.
Structured querying provides a more appropriate mechanism for retrieving information based on these defined fields than relying exclusively on semantic similarity between textual passages.
DeepRAG is intended for more extensive document synthesis and reasoning, particularly where information must be combined across multiple documents.
Option A is not the documented structured retrieval strategy. Option C is less appropriate when the task requires querying tabular records by their fields. Option D is designed for a different class of knowledge- processing operations.
The Structured strategy should therefore be selected when the primary knowledge source is CSV-formatted data requiring structured queries.
Official UiPath references/topics: Agents - Contexts - Search Strategy: Structured; Automation Suite
2025.10 - Context Grounding.
NEW QUESTION # 44
Which evaluator is best suited to verifying a boolean output that must be strictly true?
Answer: B
Explanation:
Option B correctly identifies the Deterministic Exact Match evaluator as the most appropriate mechanism for validating a strictly defined Boolean output.
UiPath provides several evaluator categories, each intended for a different validation requirement.
Exact Match compares the agent's actual output against a predefined expected value without allowing semantic substitutions or alternative representations.
For an output field that must be the Boolean value true, the evaluator should pass only when the actual result matches the expected value under the configured comparison rules.
This is important when the field controls a consequential business decision, such as whether a validation has succeeded or whether mandatory checks have been completed.
JSON Similarity examines structural similarity and is more appropriate when evaluating complex JSON representations.
LLM-as-a-Judge evaluates meaning or contextual correctness and can tolerate language variation. That flexibility is unnecessary for a strict Boolean requirement.
Trajectory evaluators inspect the agent's broader execution behavior, including intermediate actions and tool usage.
Option B therefore provides the most predictable, efficient, and reproducible verification mechanism.
The developer should also define the output schema as Boolean to distinguish the actual Boolean value true from a text string containing the word "true." Official UiPath references/topics: Agents - Evaluations - Deterministic Evaluators and Choosing the Evaluator Type.
NEW QUESTION # 45
Before publishing an agent to Orchestrator, which combination of checks ensures the agent meets enterprise readiness standards?
Answer: C
Explanation:
Option B most closely matches UiPath's documented validation benchmarks for publishing and deploying agents to Orchestrator.
UiPath recommends completing at least 30 interactive tests covering normal scenarios, boundary conditions, malformed inputs, and other representative variations.
These tests allow developers to inspect agent behavior directly and identify problems involving prompts, tool selection, contextual information, and execution outcomes.
In addition, UiPath recommends creating at least 30 curated evaluation cases and achieving an evaluation score of 70% or higher without regressions.
Evaluation results should remain consistent when prompts, tools, or model configurations change.
Option A introduces an unsupported minimum of 50 interactive tests. Option C incorrectly postpones smoke testing and treats optional guardrail logging as the primary readiness control. Option D incorrectly suggests that high evaluation accuracy eliminates the need for interactive testing.
However, the checks in B are not sufficient by themselves to guarantee enterprise readiness.
UiPath also recommends finalizing prompts, validating tool schemas, configuring necessary guardrails, confirming context sources, verifying credentials and access permissions, and testing downstream integrations.
For high-risk operations, the organization should establish stricter acceptance criteria appropriate to the business consequences of failure.
Official UiPath references/topics: Agents - Best Practices for Publishing and Deploying Agents.
Technical qualification: B omits the separate recommendation for 30 curated evaluations and absence of regressions.
NEW QUESTION # 46
When setting up a Context Grounding index in UiPath Orchestrator, which of the following describes a correct combination of source type and required setup steps?
Answer: A
Explanation:
Option B correctly describes the required configuration when creating a Context Grounding index using an Orchestrator Storage Bucket.
UiPath Context Grounding enables agents to retrieve relevant information from enterprise knowledge sources and incorporate that information into their reasoning. Before the agent can retrieve documents, the source material must be configured and indexed.
During index creation, administrators select the desired data source under Data Settings. When Storage Bucket is selected, the administrator identifies the Orchestrator shared folder containing the relevant storage bucket and specifies the appropriate file type.
The configuration supports selecting individual file types or the All option when documents have multiple supported formats. Files can subsequently be uploaded using the available upload controls.
Alternatively, a Connector source requires an Integration Service connector, an authenticated connection, and the location of the external content repository.
Option A incorrectly treats Connector sources as direct document-upload mechanisms. Option C incorrectly assumes the file type can simply remain unspecified. Option D omits the connection and source-location requirements.
Proper index configuration also requires consideration of data permissions, ingestion settings, supported document formats, and synchronization schedules.
These controls ensure that agents retrieve relevant information from authorized knowledge sources.
Official UiPath references/topics: Orchestrator - Managing Indexes; Automation Cloud - Creating Indexes - Data Settings and Storage Bucket Sources.
NEW QUESTION # 47
You need an agent to dynamically incorporate customer email content into its response.
What is the best way to pass the email content as an input argument at runtime?
Answer: C
Explanation:
Option B correctly uses UiPath Agent input arguments to introduce dynamic information during execution.
UiPath separates persistent system instructions from runtime input data. The system prompt defines the agent's role, responsibilities, behavioral constraints, and tool usage expectations.
The user prompt determines how task-specific inputs and arguments are supplied to the agent.
Developers can define input arguments through the agent's data schema and reference them in prompts using placeholders such as {{Customer_Email}}.
At runtime, the argument is populated with the actual customer email content. The agent then uses the supplied information when interpreting the request or generating its response.
This mechanism eliminates the need to modify persistent system instructions for every new email.
UiPath's current Prompt Editor also supports inserting arguments through its reference picker. The editor stores input argument references using the same placeholder representation.
Option A introduces unnecessary manual configuration and increases operational risk.
Option C incorrectly assumes that simple email text requires array indexing. Option D uses a Boolean data representation unsuitable for preserving email content.
A reliable implementation should use a descriptive text argument, establish its expected data type, and distinguish customer-provided content from trusted system instructions.
Official UiPath references/topics: Agents - Prompts - User Prompt; Agents - Prompt Editor - Input Argument References.
NEW QUESTION # 48
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