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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Prompt Engineering | 20% | - Grounding techniques
|
| AI Agents | 35% | - Agent setup and orchestration
|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Governance & Observability | 10% | - AI governance
|
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NEW QUESTION # 154
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions. Which use case functionality of Model Builder aligns with UC's request?
Answer: A
Explanation:
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
NEW QUESTION # 155
What should Universal Containers consider when deploying an Agentforce Service Agent with multiple topics and Agent Actions to production?
Answer: A
Explanation:
UC is deploying an Agentforce Service Agent with multiple topics and actions to production.
NEW QUESTION # 156
Universal Containers (UC) has registered an external service and created a template-triggered prompt flow that invokes the external service to fetch data from a REST API. UC now needs to make the response data from the external service usable inside a prompt template as a merge field when the template runs. How should UC meet this requirement?
Answer: A
Explanation:
As outlined in the AgentForce External Services and Prompt Flow Integration Guide, when data is retrieved from a registered external service via REST API, the response payload is stored as External Service Records. These records can then be referenced dynamically within prompt templates through External Service Record merge fields.
This approach allows the large language model (LLM) to use the fetched data as contextual grounding during prompt execution, ensuring that generated responses are accurate and consistent with the latest API results.
NEW QUESTION # 157
Universal Containers (UC) wants to use Generative AI Salesforce functionality to reduce Service Agent handling time by providing recommended replies based on the existing Knowledge articles.
On which AI capability should UC train the service agents?
Answer: B
Explanation:
Service Replies (specifically Einstein Service Replies) is the Salesforce Generative AI functionality designed to automatically draft responses for service agents in real-time, based on contextual information, including existing knowledge articles. This directly addresses Universal Containers' need to reduce handling time by providing recommended replies grounded in their knowledge base
NEW QUESTION # 158
Universal Containers plans to enhance the customer support team's productivity using AI. Which specific use case necessitates the use of Prompt Builder?
Answer: A
Explanation:
The use case that necessitates the use of Prompt Builder is creating a draft of a support bulletin post for new product patches. Prompt Builder allows the Agentforce Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.
NEW QUESTION # 159
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