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| Section | Weight | Objectives |
|---|---|---|
| Governance & Observability | 10% | - AI governance
|
| AI Agents | 35% | - Agent setup and orchestration
|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Prompt Engineering | 20% | - Prompt Builder usage
|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
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NEW QUESTION # 126
Universal Containers wants to incorporate CRM data as well-formatted JSON in a prompt to a large language model (LLM). What is an important consideration for this requirement?
Answer: A
Explanation:
Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language model (LLM).
The question is about an important technical or design consideration for including CRM data as JSON in that prompt.
NEW QUESTION # 127
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
Answer: B
Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.
NEW QUESTION # 128
A sales manager needs to contact leads at scale with hyper-relevant solutions and customized communications in the most efficient manner possible. Which Salesforce solution best suits this need?
Answer: C
Explanation:
The question specifies a sales manager's need to:
Contact leads at scale: Handle a large volume of leads simultaneously.
Hyper-relevant solutions: Deliver tailored solutions based on lead-specific data (e.g., CRM data, behavior).
Customized communications: Personalize outreach (e.g., emails, messages) for each lead.
Most efficient manner possible: Minimize manual effort and maximize automation.
This suggests a solution that leverages AI for personalization and automation for scale, ideally within the Salesforce ecosystem.
NEW QUESTION # 129
Universal Containers (UC) uses a file upload-based data library and custom prompt to support AI- driven training content. However, users report that the AI frequently returns outdated documents.
Which corrective action should UC implement to improve content relevancy?
Answer: B
Explanation:
UC's issue is that their file upload-based Data Library (where PDFs or documents are uploaded and indexed into Data Cloud's vector database) is returning outdated training content in AI responses. To improve relevancy by ensuring only current documents are retrieved, the most effective solution is to configure a custom retriever with a filter (Option B). In Agentforce, a custom retriever allows UC to define specific conditions-such as a filter on a "Last Modified Date" or similar timestamp field-to limit retrieval to documents updated within a recent period (e.g., last
6 months). This ensures the AI grounds its responses in the most current content, directly addressing the problem of outdated documents without requiring a complete overhaul of the data source.
NEW QUESTION # 130
Universal Containers (UC) is experimenting with using public Generative AI models and is familiar with the language required to get the information it needs. However, it can be time-consuming for both UC's sales and service reps to type in the prompt to get the information they need, and ensure prompt consistency. Which Salesforce feature should the company use to address these concerns?
Answer: A
Explanation:
UC wants to streamline the use of Generative AI by reducing the time reps spend typing prompts and ensuring consistency, leveraging their existing prompt knowledge.
NEW QUESTION # 131
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