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NEW QUESTION # 97
What is the main purpose of Prompt Builder?
Answer: C
Explanation:
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
It is not a tool for Apex programming (as in option A).
It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
Reference:
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
NEW QUESTION # 98
A sales manager is using Agent Assistant to streamline their daily tasks. They ask the agent to Show me a list of my open opportunities.
How does the large language model (LLM) in Agentforce identify and execute the action to show the sales manager a list of open opportunities?
Answer: B
Explanation:
Agentforce's LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner's role in converting intents into actionable steps while adhering to security and business logic.
NEW QUESTION # 99
An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account.
Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?
Answer: B
Explanation:
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the AI Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
* Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1
* Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta
/salesforce_ai/grounding_data_prompts
NEW QUESTION # 100
The AI Specialist of Northern Trail Outfitters reviewed the organization's data masking settings within the Configure Data Masking menu within Setup. Upon assessing all of the fields, a few additional fields were deemed sensitive and have been masked within Einstein's Trust Layer.
Which steps should the AI Specialist take upon modifying the masked fields?
Answer: B
Explanation:
After modifying masked fields inEinstein's Trust Layer, the next important step is totest and confirmthat the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.
* Option Bis correct because testing the effects of masking on AI responses is a critical step in ensuring AI continues to function as expected.
* Option A(turning off and on the Einstein Trust Layer) is unnecessary after changing the masked fields.
* Option C(turning on Einstein Feedback) allows for user feedback but is not a direct step following field masking modifications.
References:
* Salesforce Einstein Trust Layer Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer.htm
NEW QUESTION # 101
An AI Specialist 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 AI Specialist recommend to achieve this use case?
Answer: A
Explanation:
For no-code implementations, Prompt Builder allows AI 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 # 102
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