Salesforce-AI-Specialist Questions and Answers

Poor prompt performance when generating case summaries is often due to the data used for grounding being incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.

Option B (prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.

Option C (Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.

For more information, refer to Salesforce documentation on grounding AI models and data quality best practices.

The support team should use Einstein Reply Recommendations to provide quick, relevant responses to customer inquiries that are grounded in the organization’s knowledge base. This feature leverages AI to recommend accurate and consistent replies based on historical interactions and the knowledge stored in the system, ensuring that responses are aligned with organizational standards.

Einstein Service Replies (Option A) is focused on generating replies but doesn't have the same emphasis on grounding responses in the knowledge base.

Einstein Knowledge Recommendations (Option C) suggests knowledge articles to agents, which is more about assisting the agent in finding relevant articles than providing automated or AI-generated responses to customers.

Salesforce AI Specialist References:For more information on Einstein Reply Recommendations: https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations_overview.htm

To implement a solution where users enter a sales order number and the system generates a summary, the AI Specialist should create a screen flow to collect the sales order number and invoke the prompt template. The standard "Prompt Template" flow action can then be used to trigger the custom prompt, providing a summary of the sales order header and details.

Option B, creating a template-triggered prompt flow, is not necessary for this scenario because the requirement is to directly collect input through a screen flow.

Option C, using an autolaunched flow, would be inappropriate here because the solution requires user interaction (entering a sales order number), which is best suited to a screen flow.

Salesforce AI Specialist References:For further guidance on creating prompt templates with flows: https://help.salesforce.com/s/articleView?id=sf.prompt_template_flow_integration.htm

To customize the case summary response from Einstein Copilot, the AI Specialist should create a custom Record Summary prompt template for the Case object. This allows Universal Containers to tailor the way case data is summarized, ensuring the output aligns with specific business requirements or user preferences.

Option A (customizing the standard Record Summary template) does not provide the flexibility required for deep customization.

Option B (standard Copilot action) won’t allow customization; it will only use default settings.

Refer to Salesforce Prompt Builder documentation for guidance on creating custom templates for record summaries.

The "Create Close Plan" feature is designed to help sales reps by providing AI-generated strategies and steps specifically focused on closing deals. This feature leverages AI to analyze the current state of opportunities and generate a plan that outlines the actions, timelines, and key steps required to move deals toward closure. It aligns directly with the sales team’s need to meet quotas by offering actionable insights and structured plans.

Find Similar Deals (Option A) helps sales reps discover opportunities similar to their current deals but doesn’t offer a plan for closing.

Create Account Plan (Option B) focuses on long-term strategies for managing accounts, which might include customer engagement and retention, but doesn’t focus on deal closure.

Salesforce AI Specialist References:For more information on using AI for sales, visit: https://help.salesforce.com/s/articleView?id=sf.einstein_for_sales_overview.htm

In Salesforce prompt templates, instructions that guide how the Large Language Model (LLM) should generate content (in this case, personalized emails) can be included by surrounding the instruction text with triple quotes ("""). This formatting ensures that the LLM adheres to the specific instructions while generating the email content.

The use of triple quotes allows the AI to understand that the enclosed text is a directive for how to approach the task, such as limiting the content to associated opportunity information or encouraging a specific action from the recipient.

Refer to Salesforce Prompt Builder documentation for detailed instructions on how to structure prompts for generative AI.

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 AI Specialist to create and refine prompts that generate specific, relevant outputs, such as drafting support communication based on product information and patch details.

Option B (agent performance score) would likely involve predictive modeling, not prompt generation.

Option C (estimating support ticket volume) would require data analysis and predictive tools, not prompt building.

For more details, refer to Salesforce’s Prompt Builder documentation for generative AI content creation.

Einstein Copilot currently supports various actions like creating and updating records but does not support the Delete Record action. Therefore, the user's request to delete activities from the past 7 days cannot be fulfilled using Einstein Copilot.

Unsupported Action: The inability to delete records is due to the current limitations of Einstein Copilot's supported actions. It is designed to assist with tasks like data retrieval, creation, and updates, but for security and data integrity reasons, it does not facilitate the deletion of records.

User Permissions: Even if the user has the necessary permissions to delete records within Salesforce, Einstein Copilot itself does not have the capability to execute delete operations.

References:

Salesforce AI Specialist Documentation - Einstein Copilot Supported Actions:

Lists the actions that Einstein Copilot can perform, noting the absence of delete operations.

Salesforce Help - Limitations of Einstein Copilot:

Highlights current limitations, including unsupported actions like deleting records.

To create a digest of account action plans using the generative API feature, Universal Containers should use the REST API. The REST API is ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.

Metadata API is used for retrieving and deploying metadata, not for data operations like generating summaries.

SOAP API is an older API used for integration but is less flexible compared to REST for this specific use case.

For more details, refer to Salesforce REST API documentation regarding using REST for data integration and generating content.

Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem. Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.

Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.

Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.

Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.

References:

Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_llm.htm

Einstein Copilot implementation would be most advantageous in Salesforce Service Cloud when the goal is to streamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.

Option B (data security) is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.

Option C (marketing campaigns) falls outside the scope of Service Cloud and Einstein Copilot’s primary benefits, which are aimed at improving customer service and case management.

For further reading, refer to Salesforce documentation on Einstein Copilot for Service Cloud and how it improves support processes.

When creating a custom action in Einstein Copilot, one of the available options is to use Flows. Flows are a powerful automation tool in Salesforce, allowing the AI Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code.

While Apex triggers and SOQL are important Salesforce tools, Flows are the recommended method for creating custom actions within Einstein Copilot because they are declarative and highly adaptable.

For further guidance, refer to Salesforce Flow documentation and Einstein Copilot customization resources.

An AI Specialist wants to ground a new prompt template with the User related list. Grounding in prompt templates involves using data from related lists to provide context or additional information to the Large Language Model (LLM) when generating responses.

Key Consideration:

Unsupported Related Lists in Prompt Templates:

Limitation: The User related list is not supported in prompt templates for grounding purposes.

Reason: Salesforce restricts certain objects and related lists from being used in prompt templates to maintain data security and integrity. The User object often contains sensitive information and is subject to strict access controls.

Impact: Attempting to use the User related list in a prompt template will not work as expected because the system does not support it.

Why Options A and B are Incorrect:

Option A (The User related list should have View All access):

Incorrect: Even with View All access, the User related list is still not supported in prompt templates.

Security Concerns: Granting View All access to the User object is a significant security risk and not a recommended practice.

Option B (The User related list needs to be included on the record page):

Incorrect: Including the User related list on the record page does not affect its availability in prompt templates.

Irrelevance: The placement of the related list on the record page does not change the system's ability to access it in a prompt template.

References:

Salesforce AI Specialist Documentation - Prompt Templates Limitations:

Details the objects and related lists that are not supported in prompt templates.

Salesforce Help - Data Access in Prompt Templates:

Explains how data access and security considerations affect the use of objects in prompt templates.

Salesforce Trailhead - Understanding Prompt Template Grounding:

Provides insights into grounding prompt templates and the limitations involved.

Conclusion:

Since the User related list is not supported in prompt templates, the AI Specialist must consider alternative approaches. They might need to redesign the prompt template to use supported objects or related lists, or explore other methods to incorporate necessary user information while adhering to Salesforce's data access policies.

For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information, Call Explorer is the most suitable feature. Call Explorer, a part of Einstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentions and moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.

Call Summaries offer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.

Einstein Sales Insights focuses more on pipeline and forecasting insights rather than call-based analysis.

References:

Salesforce Einstein Conversation Insights Documentation: https://help.salesforce.com/s/articleView?id=einstein_conversation_insights.htm

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

When the preview button is greyed out in a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.

Option B, not saving or activating the prompt, would not necessarily cause the preview button to be greyed out, but it could prevent proper functionality.

Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.

Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.

Salesforce AI Specialist References:Refer to the documentation on troubleshooting Flex templates here: https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

When configuring a custom prompt template to use related lists, the AI 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.

Option B is correct because there is a limit on how many related list merge fields can be included in a prompt template.

Option A (text encoding) and Option C (XML/JSON rendering) are not key considerations in this context.

References:

Salesforce Prompt Builder Documentation: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm

Universal Containers needs to generate a sales proposal using data from multiple unrelated standard and custom objects within a prompt template. The most effective way to achieve this is by using a Flex template.

Flex templates in Salesforce allow AI specialists to create prompt templates that can accept inputs from multiple sources, including various standard and custom objects. This flexibility enables the direct use of data from unrelated objects without the need to create intermediary custom objects or complex flows.

References:

Salesforce AI Specialist Documentation - Flex Templates: Explains how Flex templates can be utilized to incorporate data from multiple sources, providing a flexible solution for complex data requirements in prompt templates.

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.

Option B is correct because customer churn prediction aligns with UC's need to reduce cancellations through proactive measures.

Option A (product recommendation prediction) is unrelated to contract cancellations.

Option C (contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.

References:

Salesforce Model Builder Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.model_builder_use_cases.htm

For a sales rep who may miss key details during long sales calls, the AI Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.

Call Explorer is designed for manually searching through call data but doesn't summarize.

Sales Summary is focused more on summarizing overall sales activity, not call-specific content.

For more details, refer to Salesforce's Call Summary documentation on how AI-generated summaries can improve sales rep productivity.

To monitor Einstein Copilot's usability and the assignment of actions, the AI Specialist should run Einstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types of actions it is handling, and overall user engagement with the system. It’s the most effective way to track Copilot's performance and usage patterns.

Platform Debug Logs are not relevant for tracking user behavior or the assignment of Copilot actions.

Querying the Copilot log data via the Metadata API would not provide the necessary insights in a structured manner.

For more details, refer to Salesforce's Copilot Analytics documentation for tracking AI-driven interactions.

When the "Enrich event logs with conversation data" setting is enabled in Einstein Copilot, it allows an AI Specialist or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.

This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping AI specialists track AI behavior and user engagement.

Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.

Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.

Salesforce AI Specialist References:You can refer to this documentation for further insights: https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm

The correct answer is Field Generation because this template type is designed to dynamically populate form fields with content generated by a large language model (LLM). In this scenario, leadership wants a dynamic form field that contains a summary or description generated by AI to aid customer interactions. Additionally, they want to keep a human in the loop, meaning the generated content will likely be reviewed or edited by a person before it's finalized, which aligns with the Field Generation prompt template.

Field Generation: This prompt type allows you to generate content for specific fields in Salesforce, leveraging large language models to create dynamic and contextual information. It ensures that AI content is available within the record where needed, but it allows human oversight or review, supporting the "human-in-the-loop" strategy.

Sales Email: This prompt type is mainly used for generating email content for outreach or responses, which doesn't align directly with populating fields in a form.

Record Summary: While this option might seem close, it is typically used to summarize entire records for high-level insights rather than filling specific fields with dynamic content based on AI generation.

Salesforce AI Specialist References:

You can explore more about these prompt templates and AI capabilities through Salesforce documentation and official resources on Prompt Builder: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_templates_overview.htm