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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Agents | 35% | - Agent types: Service, Sales, Employee - Agent User security and permissions - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent architecture and reasoning engine - Deterministic controls: filters and variables |
| Topic 2: Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent-to-agent communication - Agent API usage scenarios |
| Topic 3: Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Unstructured data processing: chunking, indexing - Improving response accuracy with data grounding |
| Topic 4: Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt management and user roles - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases |
| Topic 5: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Version control and maintenance - Deployment: sandbox to production |
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NEW QUESTION # 231
Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent. Which channel consideration should UC be aware of while implementing it?
Answer: B
Explanation:
Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment.
NEW QUESTION # 232
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 # 233
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities.
Answer: A
NEW QUESTION # 234
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?
Answer: A
Explanation:
In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions.
NEW QUESTION # 235
Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create a personalized introduction email. After creating a proposed draft email, which predefined adjustment should UC choose to revise the draft with a more casual tone?
Answer: C
Explanation:
When Universal Containers uses the Draft with Einstein feature in Sales Cloud to create a personalized email, the predefined adjustment to Make Less Formal is the correct option to revise the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal, making the communication more approachable while still maintaining professionalism.
Enhance Friendliness would make the tone more positive, but not necessarily more casual.
Optimize for Clarity focuses on making the draft clearer but doesn't adjust the tone.
NEW QUESTION # 236
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