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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Topic 2: Prompt Engineering | 20% | - Prompt Builder usage
|
| Topic 3: AI Agents | 35% | - Agent setup and orchestration
|
| Topic 4: Governance & Observability | 10% | - AI governance
|
| Topic 5: Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
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NEW QUESTION # 21
Universal Containers wants to test agents while preserving real data and isolating from production. Which environment should the company use with Testing Center?
Answer: C
Explanation:
The AgentForce Testing Center Implementation Guide specifies that organizations should perform all structured and regression testing in a sandbox environment replicated from production. The guide explains:
"Testing Center supports running tests safely in sandbox environments that mirror production data. This ensures realistic test conditions without impacting live systems or data."
NEW QUESTION # 22
An Agentforce Agent has been developed with multiple topics and Agent Actions that use flows and Apex. Which options are available for deploying these to production?
Answer: A
Explanation:
Why is "Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI/Metadata API" the correct answer?
When deploying an Agentforce Agent with multiple topics and Agent Actions that use flows and Apex, a complete deployment solution is required. Change sets and the Salesforce CLI/Metadata API support the deployment of flows, Apex code, and agent-related metadata.
Key Considerations for Agentforce Deployments:
Supports Deployment of All Required Components
Agentforce Agents include flows, Apex classes, topics, and agent actions.
Change sets and Salesforce CLI/Metadata API allow deployment of all these components together, ensuring a smooth transition to production.
Agentforce Metadata Can Be Deployed Using Standard Tools
Change Sets: Allows admins to move configurations, custom objects, and metadata between Salesforce environments.
Salesforce CLI/Metadata API: Enables scripted deployments, automating the transfer of Agentforce configurations.
Ensures a Complete Migration Without Manual Configuration
Deploying all components together reduces the risk of misconfiguration.
Automating deployments using the Metadata API ensures consistency across environments.
NEW QUESTION # 23
Universal Containers (UC) wants its AI agent to return responses quickly. UC needs to optimize the retriever's configuration to ensure minimal latency when grounding AI responses. Which configuration aspect should UC prioritize?
Answer: B
Explanation:
Why is "Ensure the retriever's filters are defined to limit the scope of each search efficiently" the correct answer?
In Agentforce, when optimizing a retriever's configuration to ensure minimal latency in AI- generated responses, the most effective approach is narrowing the scope of searches by applying specific filters.
Key Considerations for Optimizing Retrievers in Agentforce:
Defining Effective Filters
Applying precise search filters reduces unnecessary data retrieval, decreasing response time.
Filters help focus on relevant records, avoiding delays caused by processing large datasets.
Reducing Query Complexity
Overly broad searches can increase retrieval time, leading to latency issues.
Well-configured retriever filters streamline queries, improving response speed.
Optimizing the Data Indexing Process
Restricting retriever searches to indexed fields enhances efficiency.
Pre-indexed data is faster to access, reducing retrieval time.
NEW QUESTION # 24
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment. Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
Answer: C
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision- making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
NEW QUESTION # 25
Cloud Kicks (CK) is launching a new partner portal on Experience Cloud, CK wants to provide partners with an agent that can answer questions about product specifications from the knowledge base and allow them to submit a new Lead for a potential customer they've identified.
The agent must be accessible only to authenticated partner users on the portal. Which agent type is required to meet this scenario?
Answer: A
Explanation:
The required agent type is the Service Agent (C). The core function described-answering questions from the knowledge base-is the primary task of a Service Agent, which is designed for self-service support and knowledge article retrieval. Although the requirement also includes the ability to submit a new Lead, Service Agent models are highly configurable to include custom actions, such as a "Create Lead" action, using Agentforce Builder. Furthermore, the Service Agent type is intended to be deployed to external-facing Experience Cloud sites to provide support to external authenticated users, such as partners.
NEW QUESTION # 26
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