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What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?
A. The evaluation set can only be created using imported JSON data from previous evaluations of other agents.
B. Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.
C. Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.
D. Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.
Answer: C
Explanation:
Bis correct - creating a newevaluation setin UiPath involves a multi-step process designed to enable qualitative and quantitative review of agent behavior.
Steps include:
* Namingthe evaluation set
* Addinginput promptsandexpected outputs
* Saving each test item (often called "evaluations")
* Assigning evaluators, who will manually or automatically score the results This process enablestestable, repeatable evaluationof agent behavior before deployment - ensuring the model produces correct, useful, and safe outputs.
Options A and C are incorrect:
* A reverses the order: inputs and expected outputs are neededbeforeevaluators.
* C is false - evaluation setscan be built from scratch.D implies scoring is automatic, but human reviewers or comparison logic are often required for nuanced evaluations.
This aligns with UiPath's best practices inagent validationand post-deployment assurance.
NEW QUESTION # 44
Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?
A. Sigmoid similarity search
B. Cosine similarity search
C. ReLu similarity search
D. Softmax similarity search
Answer: B
NEW QUESTION # 45
Which of the following best describes how agents handle dynamic environments?
A. Agents adapt to changing conditions by learning.
B. Agents require complete human assistance whenever processes change.
C. Agents rely solely on static rules without contextual learning.
D. Agents fail to execute tasks when information or processes change.
Answer: A
Explanation:
Bis correct - one of the defining strengths ofUiPath's agentic automationis the ability for agents toadapt to dynamic environmentsusingLLMs and contextual grounding.
Agents differ from traditional RPA bots in that they:
* Interpret natural language
* Reason across structured and unstructured data
* Adjust outputs based onreal-time context, grounding, and updated knowledge When processes change - such as updates to escalation rules, variations in incoming requests, or new product names - agents can adjust without reprogramming, thanks to:
* Flexible prompts
* Grounded context from indexes or memory
* Few-shot or zero-shot inference capabilities
This adaptability makes agents ideal for scenarios likeemail triage,customer service, orknowledge work, where inputs and conditions vary.
Option A and D falsely suggest agents are rigid or fully dependent on human intervention.
Option C applies to classic RPA bots - not LLM-powered agents.
While agents don't"learn"in the ML retraining sense during execution, theydynamically interpret and adapt within the context of each session - a key feature enabled by UiPath's Autopilot™, Context Grounding, and agent memory frameworks.
This flexibility is foundational to deploying agents in environments whererules evolve, data flows shift, or human-like understanding is needed.
NEW QUESTION # 46
What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?
A. To keep the ECS Index stored in a shared Orchestrator folder.
B. To define static rules for retrieving data from the index.
C. To retrieve data based on the user's current session or inputs.
D. To limit the number of results retrieved from the ECS Index.
Answer: C
Explanation:
Dis correct - the primary purpose of usingContext Grounding with an ECS (Enterprise Context Service) Indexin UiPath'sAgents designer canvasis to enablereal-time, dynamic retrieval of knowledgebased on the current user session or prompt inputs.
ECS indexes are built from documents, FAQs, policies, tickets, or any enterprise content and are used to:
* Provide agents withlive knowledge grounding
* Reduce hallucinations in LLM outputs
* Support tasks like Q&A, decision-making, and summarization
When a user inputs a query, the LLM canreference the ECS indexusing similarity search (usuallycosine similarity) to pullrelevant context chunksinto the prompt. This makes the agent smarter, safer, and more accurate.
Option A relates to deployment, not purpose.
B suggests hard-coded retrieval logic, which is the opposite of dynamic grounding.
C is about tuning, not the core purpose.
Context Grounding allows agents to actcontextually and intelligently, using up-to-date organizational data
- a foundational principle in UiPath's agentic architecture.
NEW QUESTION # 47
How does adjusting the "Number of results" setting affect the agent's use of context from indexes?
A. It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
B. It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
C. It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.
D. It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.
Answer: B
Explanation:
The correct answer isC. In UiPath'sContext Groundingconfiguration, the"Number of results"setting directly affects how manychunks of indexed knowledgeare retrieved and passed to the LLM at runtime.
These chunks come from preprocessed documents and are used to build thegrounding payload- the content added to the agent's prompt for context-aware generation.
By increasing the number of results:
* The LLM has access tomore context, which can improve response quality if the added information is relevant.
* However, it alsoincreases the token load, which can reduce prompt space or introduce irrelevant noise if poorly tuned.
Reducing the number of results leads tomore focused prompts, with only top-ranked relevant chunks (based oncosine similarity) included. This is crucial when using large indexes or when LLM context windows are limited.
Option A confuses this setting with similarity threshold tuning, which is a separate parameter.
Option B is false - the agent doesnot ignore contextunless context grounding is disabled.
Option D misrepresents the function - Orchestrator folder selection is unrelated to this retrieval setting.
In summary, the "Number of results" setting allows fine-tuning ofhow much supporting context is retrieved and passed to the model. It is a key control in optimizing performance, precision, and relevance of grounded agent responses.
NEW QUESTION # 48
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