Question # 1
| Universal Containers is planning a marketing email about products that most closely match a customer's expressed interests. What should an AI Specialist recommend to generate this email? | | A. Standard email marketing template using Apex or flows for matching interest in products. | | B. Custom sales email template which is grounded with interest and product information. | | C. Standard email draft with Einstein and choose standard email template. |
B. Custom sales email template which is grounded with interest and product information.
Explanation:
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.
Question # 2
| Universal Containers wants to incorporate CRM data as well-formatted JSON in a prompt to a large language
model (LLM).
What is an important consideration for this requirement? | | A. "CRM data to JSON" checkbox must be selected when creating a prompt template.
| | B. Apex code can be used to return a JSON formatted merge field.
| | C. JSON format should be enabled in Prompt Builder Settings. |
B. Apex code can be used to return a JSON formatted merge field.
Explanation:
Context of the Question:
Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large
language model (LLM).
The question is about an important technical or design consideration for including CRM data as
JSON in that prompt.
Why Apex Code for JSON Formatting?
Apex to Generate JSON: Salesforce does not have a simple “checkbox” or single setting to
“convert CRM data to JSON.” Typically, to structure data as JSON in a template, you either:
-
Use an Apex class that queries or processes the data, then returns a JSON string.
-
Use a Flow or formula approach (though complex data structures often require Apex).
No Built-In “Enable JSON Format in Prompt Builder”: Prompt Builder doesn’t have a toggle
that automatically transforms data into JSON.
Conclusion:
The practical solution to pass CRM data in JSON format to an LLM is to use Apex code(or
a specialized Flow approach) to produce a JSON string, which the prompt can then merge and pass
along. Hence, Option B is correct.
Question # 3
| An AI Specialist configured Data Masking within the Einstein Trust Layer. How should the AI Specialist begin validating that the correct fields are being masked? | | A. Use a Flow-based resource in Prompt Builder to debug the fields’ merge values using Flow Debugger. | | B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu. | | C. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page. |
B. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Question # 4
An AI Specialist at Universal Containers is trying to set up a new Field Generation prompt template. They
take the following steps.
- Create a new Field Generation prompt template.
- Choose Case as the object type.
- Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the AI Specialist saves, tests, and activates it. Howsoever, when they go to
a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the AI
Specialist was editing the field, it was behaving as a normal field.
Which critical step did the AI Specialist miss? | | A. They forgot to reactivate the Lightning page layout for the Case object after activating their Field
Generation prompt template. | | B. They forgot that the Case Object is not supported for Add generation as Feinstein Service Replies
should be used instead. | | C. They forgot to edit the Lightning page layout and associate the field to a prompt template |
C. They forgot to edit the Lightning page layout and associate the field to a prompt template
Explanation:
For prompt templates to display the Sparkle icon (indicating Field Generation AI-generated content), the
target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the
prompt template is activated, failing to add the field to the page layout and link it to the template will result in
the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is
mandatory to enable AI-driven field interactions.
Question # 5
| In addition to Recipient and Sender, which object should an AI Specialist utilize for inserting merge fields into a Sales email template prompt? | | A. Recipient Opportunities | | B. Recipient Account | | C. User Organization |
B. Recipient Account
Question # 6
Universal Containers is considering leveraging the Einstein Trust Layer in conjunction with Einstein Generative AI Audit Data.
Which audit data is available using the Einstein Trust Layer? | | A. Response accuracy and offensiveness score | | B. Hallucination score and bias score | | C. Masked data and toxicity score |
C. Masked data and toxicity score
Explanation:
Universal Containers is considering the use of the Einstein Trust Layer along with Einstein
Generative AI Audit Data. The Einstein Trust Layer provides a secure and compliant way to
use AI by offering features like data masking and toxicity assessment.
The audit data available through the Einstein Trust Layer includes information about
masked data—which ensures sensitive information is not exposed—and thetoxicity score
, which evaluates the generated content for inappropriate or harmful language.
Question # 7
A Salesforce AI Specialist is reviewing the feedback from a customer about the ineffectiveness of the prompt template.
What should the AI Specialist do to ensure the prompt template's effectiveness?
| | A. Monitor and refine the template based on user feedback. | | B. Use the Prompt Builder Scorecard to help monitor. | | C. Periodically change the templates grounding object. |
B. Use the Prompt Builder Scorecard to help monitor.
Explanation
To address the ineffectiveness of a prompt template reported by a customer, the Salesforce AI Specialist should use the Prompt Builder Scorecard (Option B). This tool is explicitly designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy, safety, and grounding. By leveraging the scorecard, the specialist can systematically identify weaknesses in the template and make data- driven refinements. While monitoring and refining based on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce’s recommended tool for structured evaluation, aligning with documented processes for maintaining prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive and does not address the root cause.
References:
Salesforce Einstein AI Specialist Certification Guide: Emphasizes using the Prompt Builder Scorecard to evaluate prompts and iterate based on results.
Trailhead Module: "Einstein for Developers" highlights the scorecard as a critical tool for assessing prompt performance.
Salesforce Help Documentation: Details the Scorecard’s role in evaluating prompts against predefined criteria.
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FAQs of Salesforce-AI-Specialist Exam
What is the Salesforce AI Specialist Exam?
Answer: The Salesforce AI Specialist exam tests your knowledge of integrating AI into Salesforce platforms to enhance customer experiences.
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Answer: Study Guides- Utilize the official Salesforce study guide and practice by taking Practice Questions using reliable sources like RealBraindumps.
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Answer: You must score at least 73% or above to pass the Salesforce AI Specialist Exam.
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Answer: There will be 60 multiple-choice questions present in the Salesforce AI Specialist exam.
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Answer: The time for the exam is 90 minutes, so you will have plenty of time to answer all the questions.
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Answer: Yes, you can retake, but you need to wait 14 days and pay a retake fee.
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Answer: Gaining such certification enhances career opportunities through the validation of skills in AI and Salesforce; better opportunities come hand in hand with better networking, increased salary rates, and much more in a present job.
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Answer: The exam consists of 60 multiple choice questions, and it lasts 90 minutes. For More information visit: https://trailhead.salesforce.com/en/credentials/aispecialist
What are the key topic areas of the Salesforce AI Specialist Certification?
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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