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| Section | Weight | Objectives |
|---|---|---|
| Multi-Agent Interoperability | 5% | - Agent-to-agent communication - Agent API usage scenarios - Model Context Protocol (MCP) |
| Development Lifecycle | 15% | - Version control and maintenance - Monitoring, adoption and optimization - Testing agents in Testing Center - Deployment: sandbox to production |
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Unstructured data processing: chunking, indexing - Retrievers and search types: keyword, vector, hybrid - Agentforce Data Library types |
| Prompt Engineering | 20% | - Prompt management and user roles - Prompt template creation: field generation, flex types - Grounding techniques and best practices - Prompt Builder usage and use cases |
| AI Agents | 35% | - Agent types: Service, Sales, Employee - Deterministic controls: filters and variables - Agent User security and permissions - Topics and actions configuration - Channel integration: digital experience, email, Slack - Agent architecture and reasoning engine |
>> AI-201 Certification Questions <<
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NEW QUESTION # 105
Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio. Which use case works for this scenario?
Answer: A
Explanation:
For improving sales operations efficiency, Einstein Studio is ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predicting customer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions. Customer lifetime value (CLV) predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
NEW QUESTION # 106
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?
Answer: A
Explanation:
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template to populate a field with generated output, which directly aligns with the Field Generation prompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement.
Additionally, UC has enabled the Einstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whether Dynamic Fields is enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.
NEW QUESTION # 107
An Agentforce turned on Einstein Generative AI in Setup. Now, the Agentforce Specialist would like to create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in the Setup menu. What is causing the problem?
Answer: C
Explanation:
In order to access and create custom prompt templates in Prompt Builder, the Agentforce Specialist must have the Prompt Template Manager permission set assigned. Without this permission, they will not be able to access Prompt Builder in the Setup menu, even though Einstein Generative AI is enabled.
NEW QUESTION # 108
Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles.
When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?
Answer: A
Explanation:
UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud.
NEW QUESTION # 109
Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases. UC is considering implementing Agent and extending Service Cloud to mobile users. When would Agent implementation be most advantageous?
Answer: A
Explanation:
Agent implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Agent can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.
NEW QUESTION # 110
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