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| Section | Weight | Objectives |
|---|---|---|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| Governance & Observability | 10% | - AI governance
|
| Prompt Engineering | 20% | - Grounding techniques
|
| AI Agents | 35% | - Agent setup and orchestration
|
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NEW QUESTION # 145
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?
Answer: B
Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de-masking it during the response journey.
How It Works:
Data Masking in the Request Journey:
Sensitive Data Identification: Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.
Masking Sensitive Data: Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
Processing by the LLM:
Masked Input: The LLM processes the masked prompt and generates a response based on the masked data.
No Exposure of Sensitive Data: Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
De-masking in the Response Journey:
Re-insertion of Sensitive Data: After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
Providing Meaningful Responses: This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
Maintaining Data Security: At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
NEW QUESTION # 146
Universal Containers is planning a marketing email about products that most closely match a customer's expressed interests. What should An Agentforce recommend to generate this email?
Answer: A
Explanation:
To generate an email about products that closely match a customer's expressed interests, An Agentforce should recommend using a custom sales email template that is grounded with interest and product information. This ensures that the email content is personalized based on the customer's preferences, increasing the relevance of the marketing message.
Using grounding ensures that the generative AI pulls the correct data related to customer interests and product matches, making the email more effective.
NEW QUESTION # 147
An Agentforce 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 the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts. Which approach should the Agentforce Specialist recommend to achieve this use case?
Answer: A
Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
NEW QUESTION # 148
Why is starting with a narrowly scoped Agentforce use case considered a best practice?
Answer: D
Explanation:
A narrow scope limits unintended behavior and makes monitoring, tuning, and governance easier.
NEW QUESTION # 149
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: C
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 # 150
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