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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Agentforce Data Library types - Retrievers and search types: keyword, vector, hybrid |
| Topic 2: Development Lifecycle | 15% | - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance |
| Topic 3: Multi-Agent Interoperability | 5% | - Agent API usage scenarios - Agent-to-agent communication - Model Context Protocol (MCP) |
| Topic 4: AI Agents | 35% | - Channel integration: digital experience, email, Slack - Topics and actions configuration - Agent types: Service, Sales, Employee - Deterministic controls: filters and variables - Agent User security and permissions - Agent architecture and reasoning engine |
| Topic 5: Prompt Engineering | 20% | - Prompt management and user roles - Grounding techniques and best practices - Prompt Builder usage and use cases - Prompt template creation: field generation, flex types |
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NEW QUESTION # 139
Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?
Answer: A
Explanation:
Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let's evaluate the options based on its functionality and limitations.
NEW QUESTION # 140
Universal Containers is setting up the data library configuration within the Agentforce Builder.
What is true regarding Agentforce Data Libraries?
Answer: A
Explanation:
The correct statement regarding the configuration limit of Agentforce Data Libraries is that An agent can have only one data library assigned to it (C).
Agentforce Data Libraries are the mechanism by which an agent is "grounded" in an organization's internal, trusted knowledge (using Retrieval Augmented Generation or RAG). To ensure that the agent's focus remains sharp and its retrieval process is efficient and accurate, there is a one-to-one relationship between an Agentforce Agent and the Data Library it uses for grounding.
NEW QUESTION # 141
Which design choice MOST reduces the risk of hallucinations?
Answer: B
Explanation:
Grounding responses in trusted Salesforce data significantly reduces hallucinations.
NEW QUESTION # 142
Universal Containers (UC) is implementing generative AI and wants to leverage a prompt template to provide responses to customers that gives personalized product recommendations to website visitors based on their browsing history. Which initial step should UC take to ensure the chatbot can deliver accurate recommendations'
Answer: A
Explanation:
To enable personalized product recommendations using generative AI, the foundational step for Universal Containers (UC) is collecting and analyzing browsing data (Option C). Personalized recommendations depend on understanding user behavior, which requires structured data about their browsing history. Without this data, the AI model lacks the context needed to generate relevant suggestions.
Data Collection: UC must first aggregate browsing data (e.g., pages visited, products viewed, session duration) to build a dataset that reflects user preferences.
Data Analysis: Analyzing this data identifies patterns (e.g., frequently viewed categories) that inform how prompts should be structured to retrieve relevant recommendations.
Grounding in Data: Salesforce's Prompt Templates rely on grounding data to generate accurate outputs. Without analyzing browsing data, the prompt template cannot reference meaningful insights for personalization.
NEW QUESTION # 143
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: B
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 # 144
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