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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 2: Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Topic 3: Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Topic 4: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
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NEW QUESTION # 237
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts. Which approach should the Agentforce Specialist recommend to achieve this use case?
Answer: A
Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
NEW QUESTION # 238
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 # 239
Coral Cloud Resorts (CCR) sees the agent forgot the dietary/activity preferences gathered earlier.
They need those preferences to persist throughout the session. What should CCR implement?
Answer: A
Explanation:
According to the AgentForce Session Memory and Context Management Guide, when specific customer preferences (such as dietary or activity selections) must persist throughout an interaction, the correct approach is to use a context variable. The documentation states: "Context variables retain information across the user session, enabling the agent to reference prior inputs or outputs without re-asking. They are ideal for persisting customer preferences, authentication data, or ongoing session parameters."
NEW QUESTION # 240
Universal Containers has implemented an agent that answers questions based on Knowledge articles. Which topic and Agent Action will be shown in the Agent Builder?
Answer: C
Explanation:
UC's agent answers questions using Knowledge articles, configured in Agent Builder.
NEW QUESTION # 241
Coral Cloud Resorts is uploading thousands of new HTML knowledge articles files for a resort launch. To ensure Agentforce retrieves accurate responses quickly, which chunking strategy should be used when creating a new index?
Answer: C
Explanation:
In AgentForce documentation on Knowledge Indexing and Chunking Strategies, Salesforce emphasizes that when uploading large volumes of structured content such as HTML or documentation files, the system should use section-aware chunking. The guide states: "Section- aware chunking preserves the logical boundaries of headings, paragraphs, and sub-sections in structured documents like HTML or PDF files, allowing the agent to retrieve contextually accurate and relevant responses quickly." This method ensures that the agent does not split content mid-section or lose contextual relationships between headings and body text. It enhances both retrieval speed and answer precision.
NEW QUESTION # 242
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