Question # 1
| What does it mean when a prompt template version is described as immutable? | | A. Only the latest version of a template can be activated.
| | B. Every modification on a template will be saved as a new version automatically.
| | C. Prompt template version is activated; no further changes can be saved to that version. |
C. Prompt template version is activated; no further changes can be saved to that version.
Explanation:
When a prompt template version is , it means that immutable once the version is activated, it cannot be edited or modified. This ensures consistency in production environments where changes could disrupt workflows.
Option A is incorrect: Any version (not just the latest) can be activated, depending on the use case.
Option D is incorrect: Modifications require manually creating a new version; automatic versioning is not enforced.
Option C is correct: Activation locks the version, enforcing immutability.
Question # 2
An AI Specialist at Universal Containers is working on a prompt template to generate personalized emails for product demonstration requests from customers. It is important for the Al-generated email to adhere strictly to the guidelines, using only associated opportunity information, and to encourage the recipient to take the desired action.
How should the AI Specialist include these instructions on a new line in the prompt template? | | A. Surround them with triple quotes ("""). | | B. Make sure merged fields are defined. | | C. Use curly brackets {} to encapsulate instructions. |
A. Surround them with triple quotes (""").
Explanation: 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.
Question # 3
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? | | A. Run a report on the Platform Debug Logs. | | B. Query the Copilot log data using the metadata API. | | C. Run Einstein Copilot Analytics. |
C. Run Einstein Copilot Analytics.
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 # 4
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? | | A. Enable prompt test mode, allocate different prompt variations to a subset of users for
evaluation, and standardize the most effective model based on performance feedback. | | B. Create concise, clear, and consistent prompt templates with effective grounding,
contextual role-playing, clear instructions, and iterative feedback. | | C. Submit a prompt review case to Salesforce and conduct thorough testing In the
playground to refine outputs until they meet user expectations. |
B. Create concise, clear, and consistent prompt templates with effective grounding,
contextual role-playing, clear instructions, and iterative feedback.
Explanation: ForUniversal Containers (UC)to refine itsGenerative AIprompt design
strategy and improve the accuracy of the generated summaries for the custom objectGuest
, the best practice is to focus on craftingconcise, 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 Bis 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.
Question # 5
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? | | A. Turn off the Einstein Trust Layer and turn it on again. | | B. Test and confirm that the responses generated from prompts that utilize the data and masked data do not adversely affect the quality of the generated response | | C. Turn on Einstein Feedback so that end users can report if there are any negative side effects on AI features. |
B. Test and confirm that the responses generated from prompts that utilize the data and masked data do not adversely affect the quality of the generated response
Explanation: 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 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.
Question # 6
Universal Containers implemented Einstein Copilot for its users.
One user complains that Einstein Copilot is not deleting activities from the past 7 days.
What is the reason for this issue? | | A. Einstein Copilot Delete Record Action permission is not associated to the user. | | B. Einstein Copilot does not have the permission to delete the user's records. | | C. Einstein Copilot does not support the Delete Record action. |
C. Einstein Copilot does not support the Delete Record action.
Explanation: 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.
Question # 7
| What is the role of the large language model (LLM) in executing an Einstein Copilot Action? | | A. Find similar requests and provideactions that need to be executed | | B. Identify the best matching actions and correct order of execution | | C. Determine a user's access and sort actions by priority to be executed |
B. Identify the best matching actions and correct order of execution
Explanation: In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user’s request and determine the correct
sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant
and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and
governance than by the LLM.
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Answer: The key topic areas include Einstein Trust Layer, Generative AI in CRM Applications, Prompt Builder, Einstein Copilot, and Model Builder.
How does Salesforce Einstein work?
Answer: Salesforce Einstein integrates AI capabilities into Salesforce, offering tools like Sales emails, Service replies, Einstein Copilot, Prompt Builder, and more.
What are the key topic areas covered in the Salesforce AI Specialist exam?
Answer: The Salesforce AI Specialist exam covers the following key areas: 1. Einstein Trust Layer (15%), 2. Generative AI in CRM Applications (17%), 3. Prompt Builder (37%), 4. Einstein Copilot (23%), 5. and Model Builder (8%).
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