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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 20% | - Prompt Builder usage and use cases - Prompt management and user roles - Prompt template creation: field generation, flex types - Grounding techniques and best practices |
| Data Cloud for Agentforce | 20% | - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing |
| AI Agents | 35% | - Agent architecture and reasoning engine - Deterministic controls: filters and variables - Channel integration: digital experience, email, Slack - Agent types: Service, Sales, Employee - Topics and actions configuration - Agent User security and permissions |
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Development Lifecycle | 15% | - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance - Monitoring, adoption and optimization |
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NEW QUESTION # 93
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 # 94
After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?
Answer: C
Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
Low Temperature (e.g., 0.2): More deterministic, consistent responses.
High Temperature (e.g., 1.0): More creative, varied responses.
NEW QUESTION # 95
An Agentforce wants to use the related lists from an account in a custom prompt template. What should the Agentforce Specialist consider when configuring the prompt template?
Answer: B
Explanation:
When configuring a custom prompt template to use related lists, the Agentforce Specialist must be aware of the maximum number of related list merge fields that can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.
NEW QUESTION # 96
Universal Containers (UC) is rolling out an AI-powered support assistant to help customer service agents quickly retrieve relevant troubleshooting steps and policy guidelines. The assistant relies on a search index in Data Cloud that contains product manuals, policy documents, and past case resolutions. During testing, UC notices that agents are receiving too many irrelevant results from older product versions that no longer apply. How should UC address this issue?
Answer: C
Explanation:
UC's support assistant uses a Data Cloud search index for grounding, but irrelevant results from outdated product versions are an issue.
NEW QUESTION # 97
An Agentforce Specialist 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 calls by:
- Summarizing past purchases
- Displaying products the contact has shown interest in (with data
captured via Data Cloud)
- Providing a recap of past email and phone conversations that have
transcripts
Which approach should the Agentforce Specialist recommend to achieve this goal?
Answer: B
Explanation:
The AgentForce No-Code Builder and Prompt Template Guide specifies that when using Salesforce's standard foundation models, users can ground prompts on CRM and Data Cloud data without requiring fine-tuning or model deployment. The documentation notes: "No-code users can leverage standard foundation models with prompt templates grounded in Salesforce data, including CRM and Data Cloud, to summarize records, highlight insights, and prepare agents for customer engagements." This approach allows the sales operations team to automatically summarize customer data (purchases, interests, and communications) without needing model customization.
NEW QUESTION # 98
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