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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance & Observability | 10% | - AI governance
|
| Topic 2: Prompt Engineering | 20% | - Prompt Builder usage
|
| Topic 3: Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Topic 4: Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Topic 5: AI Agents | 35% | - Agent behavior configuration
|
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NEW QUESTION # 143
Universal Containers (UC) wants to make a sales proposal and directly use data from multiple unrelated objects (standard and custom) in a prompt template. How should UC accomplish this?
Answer: D
Explanation:
UC needs to incorporate data from multiple unrelated objects (standard and custom) into a prompt template for a sales proposal.
NEW QUESTION # 144
Coral Cloud Resorts is implementing Agentforce retrieval. Customers sometimes type ambiguous terms (for example, "package" could mean vacation package or baggage). Which retrieval strategy best balances precision and contextual disambiguation?
Answer: B
Explanation:
According to the AgentForce Retrieval Optimization Guide, when handling ambiguous search terms such as "package," which may refer to multiple concepts, the recommended approach is to use hybrid search. The documentation defines hybrid search as: "A combined retrieval method that leverages keyword-based precision and semantic embeddings to capture contextual intent.
This approach ensures high recall while maintaining exact-term precision." This method allows AgentForce to resolve ambiguity by using semantic context to interpret meaning while maintaining keyword-based precision for deterministic matching. The guide further notes: "Hybrid retrieval offers the optimal balance between contextual understanding and exact- term accuracy, especially in multi-domain or ambiguous queries." In contrast, semantic search only may misinterpret terms without adequate context, and keyword search only lacks the contextual reasoning to differentiate between meanings. Thus, Option A aligns with Salesforce's documented best practice for retrieval precision and contextual relevance.
NEW QUESTION # 145
Which part of the Einstein Trust Layer architecture leverages an organization's own data within a large language model (LLM) prompt to confidently return relevant and accurate responses?
Answer: C
Explanation:
Dynamic Grounding in the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched with organization-specific data (e.g., Salesforce records, Knowledge articles) to generate accurate and relevant responses. By dynamically injecting contextual data into prompts, it reduces hallucinations and aligns outputs with trusted business data.
NEW QUESTION # 146
In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can An Agentforce change?
Answer: B
Explanation:
In Model Playground, An Agentforce working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce's official documentation on foundational model adjustments.
NEW QUESTION # 147
An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?
Answer: C
Explanation:
The task requires analyzing user inputs, requests, and queries to identify patterns and trends in Agentforce interactions.
NEW QUESTION # 148
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