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| Section | Weight | Objectives |
|---|---|---|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Prompt Engineering | 20% | - Grounding techniques
|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| AI Agents | 35% | - Agent setup and orchestration
|
| Governance & Observability | 10% | - AI governance
|
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NEW QUESTION # 254
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?
Answer: C
Explanation:
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement.
Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.
NEW QUESTION # 255
A sales rep at Universal Containers is extremely busy and sometimes will have very long sales calls on voice and video calls and might miss key details. They are just starting to adopt new generative AI features. Which Einstein Generative AI feature should An Agentforce recommend to help the rep get the details they might have missed during a conversation?
Answer: C
Explanation:
For a sales rep who may miss key details during long sales calls, the Agentforce Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.
Call Explorer is designed for manually searching through call data but doesn't summarize.
Sales Summary is focused more on summarizing overall sales activity, not call-specific content.
NEW QUESTION # 256
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI- generated responses. UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?
Answer: A
Explanation:
In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the `Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.
NEW QUESTION # 257
What is the purpose of applying filters in a custom retriever configuration?
Answer: B
Explanation:
The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.
For instance, an AgentForce retriever could be configured to include only documents tagged as
"Active" or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.
NEW QUESTION # 258
Which design choice MOST reduces the risk of hallucinations?
Answer: C
Explanation:
Grounding responses in trusted Salesforce data significantly reduces hallucinations.
NEW QUESTION # 259
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