Salesforce Certified AI Specialist Practice Test - Questions Answers, Page 7
List of questions
Question 61

An Al Specialist is tasked with configuring a generative model to create personalized sales emails using customer data stored in Salesforce. The AI
Specialist has already fine-tuned a large language model (LLM) on the OpenAI platform. Security and data privacy are critical concerns for the client.
How should the AI Specialist integrate the custom LLM into Salesforce?
Explanation:
Since security and data privacy are critical, the best option for the AI Specialist is to integrate the fine-tuned LLM (Large Language Model) into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce's environment, adhering to data privacy standards.
Option A (embedding via iFrame) is less secure and doesn't integrate deeply with Salesforce's data and security models.
Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.
Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce's Einstein Studio documentation on integrating external models.
Question 62

What should an AI Specialist consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?
Explanation:
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.
Question 63

Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create a personalized introduction email.
After creating a proposed draft email, which predefined adjustment should UC choose to revise the draft with a more casual tone?
Explanation:
When Universal Containers uses the Draft with Einstein feature in Sales Cloud to create a personalized email, the predefined adjustment to Make Less Formal is the correct option to revise the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal, making the communication more approachable while still maintaining professionalism.
Enhance Friendliness would make the tone more positive, but not necessarily more casual.
Optimize for Clarity focuses on making the draft clearer but doesn't adjust the tone.
For more details, see Salesforce documentation on Einstein-generated email drafts and tone adjustments.
Question 64

Universal Containers recently launched a pilot program to integrate conversational AI into its CRM business operations with Einstein Copilot.
How should the AI Specialist monitor Copilot's usability and the assignment of actions?
Explanation:
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.
Question 65

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 a Salesforce AI Specialist recommend to address these concerns?
Explanation:
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.
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm
Question 66

Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft email contains placeholders for attributes that could have been derived from the recipient's contact record.
What is the most likely explanation for why the draft email shows these placeholders?
Explanation:
When using Einstein Generative AI to create personalized emails, if placeholders appear in the draft email where data from a recipient's Contact record should be, the most likely reason is that the user lacks permission to access the necessary fields. Salesforce's field-level security may prevent users from viewing or utilizing certain data fields, resulting in placeholders being shown instead of the actual values.
Option B is correct because missing field permissions will cause placeholders in email drafts.
Option A (missing Einstein Sales Emails permission) is unlikely, as this would prevent email generation altogether, not just placeholders.
Option C (locale language issues) would more likely affect language-specific issues, not field placeholders.
Salesforce Email Template and Permissions Documentation: https://help.salesforce.com/s/articleView?id=sf.email_templates_field_permissions.htm
Question 67

Universal Containers (UC) has implemented Generative AI within Salesforce to enable summarization of a custom object called Guest. Users have reported mismatches in the generated information.
In refining its prompt design strategy, which key practices should UC prioritize?
Explanation:
For Universal Containers (UC) to refine its Generative AI prompt design strategy and improve the accuracy of the generated summaries for the custom object Guest, the best practice is to focus on crafting concise, clear, and consistent prompt templates. This includes:
Effective grounding: Ensuring the prompt pulls data from the correct sources.
Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.
Clear instructions: Giving unambiguous directions on what to include in the response.
Iterative feedback: Regularly testing and adjusting prompts based on user feedback.
Option B is correct because it follows industry best practices for refining prompt design.
Option A (prompt test mode) is useful but less relevant for refining prompt design itself.
Option C (prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.
Salesforce Prompt Design Best Practices: https://help.salesforce.com/s/articleView?id=sf.prompt_design_best_practices.htm
Question 68

An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?
Explanation:
To ground a sales email on Opportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use a prompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
Option B (flex template) does not provide the ability to fetch dynamic data from Salesforce records automatically.
Option C (manual insertion) would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer to Salesforce documentation on flows and grounding for more details on integrating data into custom prompt templates.
Question 69

Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the 'Save As' function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?
Explanation:
When Universal Containers creates a new Sales Email prompt template using the 'Save As' function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the AI Specialist should manually add the necessary hyperparameters to the new template.
Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
Option A (Model Playground) is not necessary here, as it focuses on fine-tuning models, not adjusting templates directly.
Option C (Reverting to the standard template) does not solve the issue of customizing the prompt template.
For more information, refer to Prompt Builder documentation on configuring hyperparameters in custom templates.
Question 70

Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases. UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users.
When would Einstein Copilot implementation be most advantageous?
Explanation:
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.
Question