Salesforce Salesforce-AI-Specialist Salesforce Certified AI Specialist Exam Exam Practice Test

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.

When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.

Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.

Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.

For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.

To enable Universal Containers' sales team with automatic post-call visibility into mentions of competitors, products, and custom phrases, the AI Specialist should set up Call Insights. Call Insights analyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.

Call Summaries provide a general overview of the call but do not specifically highlight keywords or topics.

Call Explorer is a tool for navigating through call data but does not focus on automatic insights.

For more information, refer to Salesforce's Call Insights documentation regarding the analysis of call content and extracting actionable information.

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

To generate an email about products that closely match a customer’s expressed interests, an AI Specialist 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.

For more information, refer to Salesforce documentation on grounding AI-generated content and email personalization strategies.

When using Prompt Builder in a Salesforce org, the correct process involves several important steps:

Select the appropriate prompt template type based on the use case.

Develop the prompt within the prompt workspace, where the template is created and customized.

Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.

Pick the model to use for generating responses, either using Salesforce's built-in models or custom ones.

Test and validate the generated responses to ensure accuracy and effectiveness.

Option B is correct as it follows the proper steps for using Prompt Builder.

Option A and Option C do not capture the full process correctly.

References:

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

The Einstein Trust Layer is designed to ensure transparency, compliance, and security for organizations leveraging Salesforce’s AI and generative AI capabilities. Specifically, for auditing, tracking, and viewing masked data, organizations can utilize:

Audit Trail in Data Cloud: The audit trail captures and stores all prompts submitted to large language models (LLMs), ensuring that sensitive or masked data interactions are logged. This allows organizations to monitor and audit all AI-generated outputs, ensuring that data handling complies with internal and regulatory guidelines. The Data Cloud provides the infrastructure for managing and accessing this audit data.

Why not B? Using Prompt Builder in Setup to send prompts to the LLM is for creating and managing prompts, not for auditing or tracking data. It does not interact directly with the audit trail functionality.

Why not C? Although the audit trail can be accessed in Setup, the user-generated prompts are primarily tracked in the Data Cloud for broader control, auditing, and analysis. Setup is not the primary tool for exporting or managing these audit logs.

More information on auditing AI interactions can be found in the Salesforce AI Trust Layer documentation, which outlines how organizations can manage and track generative AI interactions securely.

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

To improve the productivity of the service center, the AI Specialist should recommend the Service Replies and Case Summaries features.

Service Replies helps agents by automatically generating suggested responses to customer inquiries, reducing response time and improving efficiency.

Case Summaries provide a quick overview of case details, allowing agents to get up to speed faster on customer issues.

Work Summaries are not as relevant for direct customer service operations, and Sales Summaries are focused on sales processes, not service center productivity.

For more information, see Salesforce's Einstein Service Cloud documentation on the use of generative AI to assist customer service teams.

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.

Action Builder is the central place in Salesforce Agentforce where you define and manage actions that your AI agents can perform. This includes connecting actions to various tools and systems.

Topics in Agentforce represent the different tasks or intents that an AI agent can handle. By assigning an action to a Topic in Action Builder, you're essentially telling the agent, "When you encounter this type of request or situation, perform this action."

For Universal Containers (UC), to reduce the time and ensure prompt consistency when using public generative AI models, the recommended feature is Einstein Prompt Builder and Prompt Templates. This feature allows teams to create reusable and consistent prompts for generative AI tasks, ensuring that all users receive uniform responses without having to type in detailed prompts manually every time.

Einstein Prompt Builder simplifies the creation of prompts, and Prompt Templates standardize the inputs, saving time for sales and service reps.

Option A (Einstein Recommendation Builder) is more focused on recommendations, not prompt standardization.

Option B (Einstein Copilot Action: Query Records) is for querying records, not generating AI-driven prompts.

References:

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

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

When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the AI Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.

Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.

Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.

The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.

Salesforce AI Specialist References:For more information, refer to: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm

In Salesforce, Generative AI capabilities are controlled by specific permission sets. To use features such as generating summaries with AI, users need to have the correct permission sets that allow access to these functionalities.

Generative AI User Permission Set: This is a key permission set required to enable the generative AI capabilities for a user. In this case, the missing Generative AI User permission set prevents the user from seeing the generative AI-enabled field icon. Without this permission, the generative AI feature in the Competitor Analysis custom object won't be accessible.

Why not A? The Prompt Template User permission set relates specifically to users who need access to prompt templates for interacting with Einstein GPT, but it's not directly related to the visibility of AI-enabled field icons.

Why not B? While a prompt template might need to be activated, this is not the primary issue here. The question states that other users with the same profile can see the icon, so the problem is more likely to be permissions-based for this particular user.

For more detailed information, you can review Salesforce documentation on permission sets related to AI capabilities at Salesforce AI Documentation and Einstein GPT permissioning guidelines.

Service AI Grounding is the solution that Universal Containers should use to ensure Einstein AI drafts responses based on a well-defined data source. Service AI Grounding allows the AI model to be anchored in specific, relevant data sources, ensuring that any AI-generated responses (e.g., email replies) are accurate, relevant, and drawn from up-to-date information, such as Knowledge articles or cases.

Given that UC has legacy fields and outdated data, Service AI Grounding ensures that only the valid and applicable data is used by Einstein AI to craft responses. This helps improve the relevance of responses and avoids inaccuracies caused by outdated or irrelevant fields.

Work Summaries and Service Replies are useful features but do not address the need for grounding AI outputs in specific, current data sources like Service AI Grounding does.

For more details, you can refer to Salesforce’s Service AI Grounding documentation for managing AI-generated content based on accurate data sources.

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.

When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder's Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.

Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with dynamic debugging.

Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.

Salesforce AI Specialist References:To explore more about dynamic debugging in Copilot Builder, see: https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm

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

After modifying masked fields in Einstein's Trust Layer, the next important step is to test and confirm that 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 B is 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

Salesforce has restrictions on which objects and related lists can be used for grounding prompt templates. This is likely due to security and privacy concerns related to user data.

While it might seem intuitive to use the User related list to provide context to the LLM, Salesforce prevents this to ensure that sensitive user information is not inadvertently exposed or misused.

Therefore, the AI Specialist needs to explore alternative ways to incorporate the necessary user information into the prompt template, perhaps by using other related objects or fields that are supported.

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.

For Universal Containers (UC) to offer personalized service experiences and reduce agent handling time using AI-generated responses grounded in the Knowledge base, the best solution is Einstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and the Knowledge base, ensuring accuracy and relevance while saving time for service agents.

Einstein Email Replies (option A) is more suited for sales use cases.

Einstein Generative Service Replies for Email (option C) could be a future offering, but as of now, Einstein Service Replies for Email is the correct choice for grounded, knowledge-based responses.

References:

Einstein Service Replies Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_service_replies.htm

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.

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.

In Model Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:

Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.

Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.

Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.

These hyperparameters are adjustable to fine-tune the model’s responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.

For more details, refer to Salesforce AI Model Playground guidance from Salesforce’s official documentation on foundational model adjustments.

Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:

No data is used for training or product improvements by third-party LLMs.

No data is retained outside of the customer's Salesforce organization.

The LLM provider cannot access any customer data.

This policy aligns perfectly with UC’s requirements for keeping their data safe while leveraging generative AI capabilities.

Prompt Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.

References:

Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm

Before creating a Field Generation prompt template, the AI Specialist must ensure that the Salesforce org is set to API version 59 or higher. This version of the API introduces support for advanced generative AI features, such as enabling fields for generative AI outputs. This is a critical technical requirement for the Field Generation prompt template to function correctly.

Option A (rich text field requirement) is not necessary for generative AI functionality.

Option C (Dynamic Forms) does not impact the ability of a field to be generative AI-enabled, although it might enhance the user interface.

For more information, refer to Salesforce documentation on API versioning and Field Generation templates.