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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Context & Knowledge Management | 20% | - Data sources and retrieval methods - Context packages and grounding - Knowledge base integration |
| Topic 2: Orchestration & Human-in-the-Loop | 20% | - Escalations and Action Center - Service tasks and agent invocation - UiPath Maestro and BPMN modeling |
| Topic 3: Agentic Automation Fundamentals | 20% | - Core principles of agentic automation - AI, LLM and generative AI concepts - Agents vs traditional robots |
| Topic 4: Agent Design & Development | 25% | - Tools, connections and integration services - Prompt engineering best practices - Agent Builder in Studio Web |
| Topic 5: Governance, Evaluation & Trust | 15% | - Agent performance evaluation - Guardrails and responsible AI - Observability and reliability |
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NEW QUESTION # 19
Why is an agent story important in the development life-cycle?
Answer: B
Explanation:
The correct answer isD, and this is a foundational concept in UiPath'sAgentic Discovery and Design Blueprint methodology.
Anagent storyserves as aclear, narrative-driven blueprintthat describes:
* What the agent does
* For whom it works
* When it activates
* How it makes decisions
* What success looks like
UiPath emphasizes that a well-crafted agent story ensures alignment betweenbusiness stakeholders,subject matter experts (SMEs), andtechnical developers. It keeps the development team focused on value delivery by outlining thecore capabilities,contextual behavior, andinteractionsof the agent in a human-readable form.
This approach is critical during thedesign phase, as it:
* Prevents scope creep
* Clarifies success metrics
* Enhances stakeholder buy-in
* Anchors prompt design, orchestration, and escalation logic
UiPath also uses the agent story to guidegrounding strategies, tool selection, and even escalation paths - making it much more than a documentation artifact.
Options A, B, and C misrepresent the function of agent stories. Only D captures its value in focusing the team onwhat matters most for delivering real business outcomes.
NEW QUESTION # 20
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Answer: C
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 # 21
A team is designing an agent to convert plain text meeting notes into a formatted agenda (e.g., structured bullet points). Despite providing a few example transformations in the prompt, the agent generates agendas in inconsistent formats. What critical step was likely overlooked?
Answer: C
Explanation:
This is a repeat of Question 16, and the correct answer remains A.
Even when few-shot examples are included, omitting clear formatting instructions leads to inconsistent outputs, which can break downstream processes in agentic automation.
UiPath's Prompt Engineering guidance emphasizes that instruction clarity is as important as examples - especially when output format matters (like agendas, classifications, or structured text).
An optimal prompt includes:
A task description (e.g., "Convert meeting notes into a 3-section agenda") Clear format instructions (e.g., use bullet points, bold headers) Few-shot examples Optional constraints like length or tone Without that first element - clear instructions - the LLM has to guess the output format, leading to variance and unreliability.
NEW QUESTION # 22
What is the significance of the "as-is" process map in identifying agentic automation opportunities?
Answer: C
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 # 23
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
Answer: C
NEW QUESTION # 24
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