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Latest Salesforce-AI-Specialist Dumps Ppt | Salesforce-AI-Specialist Exam Pattern

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.

Topic 2

Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.

Topic 3

Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.

Topic 4

Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.

Topic 5

Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.

Salesforce Certified AI Specialist Exam Sample Questions (Q148-Q153):

NEW QUESTION # 148
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.

Answer: B

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.
* ConclusionThe practical solution to pass CRM data in JSON format to an LLM is touse Apex code(or a specialized Flow approach) to produce a JSON string, which the prompt can then merge and pass along. Hence,Option Bis correct.
Salesforce AI Specialist References & Documents
* Salesforce Documentation:Working with JSON in ApexDescribes how to serialize and deserialize data using Apex for integration or AI prompts.
* Salesforce AI Specialist Study GuideEmphasizes the need for custom logic (often in Apex) when complex data transformations (like JSON formatting) are required.

NEW QUESTION # 149
Universal Containers (UC) has a mature Salesforce org with a lot of data in cases and Knowledge articles. UC is concerned that there are many legacy fields, with data that might not beapplicable for Einstein AI todraft accurate email responses.
Which solution should UC use to ensure Einstein AI can draft responsesfrom a defined data source?

A. Service Replies
B. Work Summaries
C. Service AI Grounding

Answer: C

Explanation:
Service AI Groundingis the solution thatUniversal Containersshould use to ensureEinstein AIdrafts 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 asKnowledge articlesorcases.
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 SummariesandService Repliesare useful features but do not address the need for grounding AI outputs in specific, current data sources likeService AI Groundingdoes.
For more details, you can refer to Salesforce'sService AI Grounding documentationfor managing AI- generated content based on accurate data sources.

NEW QUESTION # 150
An AI Specialist is considering using a Field Generation prompt template type.
What should the AI Specialist check before creating the Field Generation prompt to ensure it is possible for the field to be enabled for generative AI?

A. That the Lightning page layout where the field will reside has been upgraded to Dynamic Forms
B. That the org is set to API version 59 or higher
C. That the field chosen must be a rich text field with 255 characters or more.

Answer: B

Explanation:
Before creating aField Generation prompt template, the AI Specialist must ensure that the Salesforceorg 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 toSalesforce documentation on API versioningandField Generation templates.

NEW QUESTION # 151
Which part of the Einstein Trust Layer architecture leverages an organization's own data within a large language model (LLM) prompt to confidently return relevant and accurate responses?

A. Data Masking
B. Prompt Defense
C. Dynamic Grounding

Answer: C

Explanation:
Dynamic Grounding in the Einstein Trust Layer architecture ensures that large language model (LLM) prompts are enriched with organization-specific data (e.g., Salesforce records, Knowledge articles) to generate accurate and relevant responses. By dynamically injecting contextual data into prompts, it reduces hallucinations and aligns outputs with trusted business data.
* Prompt Defense (A) focuses on blocking malicious inputs or prompt injections but does not enhance responses with organizational data.
* Data Masking (B) redacts sensitive information but does not contribute to grounding responses in business context.

NEW QUESTION # 152
Universal Containers is using Einstein Copilot for Sales to find similar opportunities to help close deals faster.
The team wants to understand the criteria used by the copilot to match opportunities.
What is one criteria that Einstein Copilot for Sales uses to match similar opportunities?

A. Matched opportunities were created in the last 12 months.
B. Matched opportunities have a status of Closed Won from last 12 months.
C. Matched opportunities are limited to the same account.

Answer: B

Explanation:
WhenEinstein Copilot for Salesmatches similar opportunities, one of the primary criteria used is whether the opportunities have astatus of Closed Wonwithin thelast 12 months. This is a key factor in identifying successful patterns that could help close current deals. By focusing on opportunities that have been recently successful, Einstein Copilot can provide relevant insights and suggestions to sales reps to help them close similar deals faster.
For more information, reviewSalesforce Einstein Copilot documentationrelated toopportunity matching and sales success patterns.

NEW QUESTION # 153
......

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