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| Section | Weight | Objectives |
|---|---|---|
| Orchestration & Human-in-the-Loop | 20% | - Service tasks and agent invocation - UiPath Maestro and BPMN modeling - Escalations and Action Center |
| Governance, Evaluation & Trust | 15% | - Observability and reliability - Guardrails and responsible AI - Agent performance evaluation |
| Context & Knowledge Management | 20% | - Data sources and retrieval methods - Context packages and grounding - Knowledge base integration |
| Agentic Automation Fundamentals | 20% | - Core principles of agentic automation - Agents vs traditional robots - AI, LLM and generative AI concepts |
| Agent Design & Development | 25% | - Prompt engineering best practices - Agent Builder in Studio Web - Tools, connections and integration services |
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NEW QUESTION # 30
What is one of the key benefits of providing RAG as a service to UiPath generative AI experiences?
Answer: C
Explanation:
The correct answer is A - RAG (Retrieval-Augmented Generation) enhances generative AI experiences in UiPath by providing grounded, context-relevant data at runtime, which significantly reduces hallucinations.
Here's how it works:
When an LLM receives a query, RAG pulls relevant documents or snippets from enterprise data sources (like knowledge bases, SharePoint, Confluence).
This content is passed to the LLM as context, enabling the model to respond using ground truth, not generic or fabricated knowledge.
UiPath's GenAI platform and agentic agents use RAG to:
Enrich prompt context
Drive document-based answers
Support fact-checked decisions in customer service, HR, IT, etc.
Option B is false - RAG doesn't alter the LLM's context window.
C is incorrect - RAG works because it queries live knowledge bases.
D is wrong - RAG supports real-time dynamic data, not just historical.
NEW QUESTION # 31
What is the significance of the "as-is" process map in identifying agentic automation opportunities?
Answer: B
Explanation:
The correct answer isA- the"as-is" process mapis a foundational tool in UiPath'sAgentic Blueprint Design framework. It visualizes thecurrent stateof how work is performed, enabling teams to:
* Identifymanual, repetitive, or time-consuming tasks
* Spotdelays, rework loops, or human bottlenecks
* Discover areas whereagents can augment or replace tasks
It acts as abaseline diagnostic, not an implementation-ready flow.
This is a key step in workshops led byprocess owners or business SMEs, before designing the "to-be" process where agents are introduced.
B is incorrect - the as-is map isnot final.
C refers to the optimized state, not the current one.
D confuses the mapping's role - it captures thepresent, not thegoals.
NEW QUESTION # 32
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Answer: B
Explanation:
Cis correct - UiPath'sContext Groundingsystem usescosine similarity searchto retrieve the most relevant documents, passages, or semantic chunks from indexed data during runtime.
This retrieval process is critical for:
* Minimizing hallucination
* Enhancingprompt grounding
* Connecting agents toreal-time enterprise knowledge
Here's how it works:
* All documents areembedded into vector spaceusing a model (e.g., OpenAI or Azure OpenAI embeddings)
* At runtime, the agent's query isconverted into a vector
* Acosine similaritycomparison is used to fetch themost semantically similar documentsbased onangle (not magnitude)between vectors This enables agents to provideaccurate, contextual answers, like referencing HR policies or IT guides.
Other options:
* A (Softmax) is a classifier, not a search method
* B (Sigmoid) is for binary classification
* D (ReLU) is an activation function in neural nets - not for search
NEW QUESTION # 33
What are the primary benefits of Context Grounding when querying data across multiple documents?
Answer: C
Explanation:
Dis correct -Context Groundingin UiPath usessemantic search across indexed contentto provide relevant and meaningful context to the agent, even when the data spansmultiple documents.
This capability is powered by:
* Embedding-based similarity search(e.g., cosine similarity)
* Intelligent chunking and indexing of enterprise data
* Runtime query matching based on theagent's prompt or user input
This enables agents to:
* Retrieverelevant information across distributed content
* Detectrelationships between topics, even if data is fragmented
* Supportmulti-document summarization,comparison, andknowledge-based reasoning For example, an agent could compare policy details across multiple HR documents to generate a unified response or identify inconsistencies in invoice records spread across different files.
Option A is false -Context Grounding is automaticonce indexing is configured.
B is incorrect - it's explicitly designed toquery across documents.
C misrepresents the system - it doesn't extract random text; it retrievessemantically relevantpassages based on the LLM's intent.
This powerful grounding mechanism makes UiPath agentsintelligent, context-aware, and enterprise-ready, especially in knowledge-intensive environments.
NEW QUESTION # 34
Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?
Answer: D
Explanation:
The correct answer isB- UiPath'sAgentic Blueprint Design processemphasizes the importance of grounding automation opportunities inreal user context and operational pain points.
During thevalidation stage, developers and stakeholders assess:
* Specific persona rolesand responsibilities
* Current pain pointsandtime-consuming tasks
* Impact potentialof agent assistance
This ensures the agent story reflectsvalue-driven automation, not just technical ambition. It also validates that the agent solves areal bottleneck- such as handling repetitive approvals, prioritizing requests, or managing context-based escalations.
UiPath warns against the pitfalls outlined in A, C, and D:
* A and D overlookpersona-centricity, which is essential.
* C skips the criticaldiscovery and mappingphase that should come before prototyping.
By focusing on B, teams ensure their agent ispurpose-builtfor the right users, delivering measurable outcomes aligned to business needs.
NEW QUESTION # 35
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