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| Section | Weight | Objectives |
|---|---|---|
| AI Agents | 35% | - Agent User security and permissions - Topics and actions configuration - Agent types: Service, Sales, Employee - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables - Agent architecture and reasoning engine |
| Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding - Agentforce Data Library types |
| Development Lifecycle | 15% | - Deployment: sandbox to production - Monitoring, adoption and optimization - Version control and maintenance - Testing agents in Testing Center |
| Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt management and user roles - Prompt Builder usage and use cases |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
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NEW QUESTION # 17
The marketing team at Universal Containers is looking for a way personalize emails based on customer behavior, preferences, and purchase history. Why should the team use Agent as the solution?
Answer: C
Explanation:
Agent is designed to assist in generating personalized, AI-driven content based on customer data such as behavior, preferences, and purchase history. For the marketing team at Universal Containers, this is the perfect solution to create dynamic and relevant email content. By leveraging Agent, they can ensure that each customer receives tailored communications, improving engagement and conversion rates.
NEW QUESTION # 18
Universal Containers (UC) recently attended a major trade show and received thousands of new leads from event badge scans. UC is struggling to follow up with each lead in a timely, personalized way. Leadership wants to:
- Qualify and nurture leads 24/7.
- Provide accurate answers to prospect questions.
- Automatically book meetings with qualified prospects.
- Free up reps to focus on building relationships and closing deals.
Which Agentforce capability should UC implement to meet these goals?
Answer: A
Explanation:
Universal Containers (UC) needs a solution that can automatically qualify and nurture thousands of new leads 24/7, provide accurate and consistent responses to prospects, schedule meetings for qualified leads, and allow sales representatives to focus on relationship building and closing deals. These needs align precisely with the Agentforce SDR Agent.
According to official AgentForce documentation, "Agentforce SDR helps sales teams qualify and nurture leads at scale, around the clock. It acts as a digital sales development representative capable of engaging new leads instantly, asking the right qualifying questions, answering inquiries accurately using connected Salesforce data, and automatically scheduling meetings on behalf of the sales team." The documentation further explains that the SDR Agent is designed to "personalize outreach, manage follow-up sequences, and book meetings directly from your website or campaign pages." This automation "frees your human reps to focus on high-value interactions and closing opportunities rather than manual lead qualification." By contrast, the Sales Coach capability focuses on guiding and coaching sales representatives internally rather than interacting with prospects, and the Commerce Agent is designed for e- commerce use cases such as assisting shoppers with product discovery and order management- not lead nurturing.
NEW QUESTION # 19
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: C
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 # 20
An Agentforce is creating a custom action for Agentforce. Which setting should the a ensure the action performs as expected?
Answer: C
Explanation:
When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.
Salesforce documentation emphasizes that Action Instructions directly impact the bot's ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot's behavior with business requirements.
NEW QUESTION # 21
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding. What should UC consider when using standard Service AI Grounding?
Answer: A
Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses.
Key considerations:
NEW QUESTION # 22
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