SALESFORCE-AI-SPECIALIST PDF EXAM DUMP | DUMPS SALESFORCE-AI-SPECIALIST QUESTIONS

Salesforce-AI-Specialist Pdf Exam Dump | Dumps Salesforce-AI-Specialist Questions

Salesforce-AI-Specialist Pdf Exam Dump | Dumps Salesforce-AI-Specialist Questions

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 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.

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Dumps Salesforce-AI-Specialist Questions | Salesforce-AI-Specialist Valid Exam Simulator

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Salesforce Certified AI Specialist Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system.
Which data grounding technique should the AI Specialist recommend?

  • A. Eternal Object Record Merge Fields
  • B. External Services Merge Fields
  • C. Apex Merge Fields

Answer: A

Explanation:
* Context of the Requirement:Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
* Data Grounding in LLM Prompts:Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to useExternal Objects(via Salesforce Connect) when data resides outside of Salesforce.
* Why External Object Record Merge Fields:
* External Objectsappear much like standard or custom objects but map to tables in external systems.
* You can reference fields from these External Objects in merge fields, allowing real-time data retrieval from the external ERP system without storing that data natively in Salesforce.
* This is a simpler "point-and-reference" approach compared to coding custom Apex or configuring external services for direct prompt embedding.
* Why Not External Services Merge Fields or Apex Merge Fields:
* External Services Merge Fieldstypically leverage flows or external service definitions. While feasible, it is more about orchestrating or invoking external services for automation (e.g., Flow).
It's not the standard approach for seamlessly referencingexternal recorddata in prompt merges.
* Apex Merge Fieldswould imply custom Apex code controlling the prompt insertion. While possible, it's less "clicks not code" friendly and is not the default method for referencing typical record data.
* References and Study Resources:
* Salesforce Help & Training#Salesforce Connect and External Objects
* Salesforce Trailhead#"Integrate External Data with Salesforce Connect"
* Salesforce AI Specialist Study Resources(documentation regarding how to ground LLM prompts using External Objects)


NEW QUESTION # 79
Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases.
Which feature should the AI Specialist set up to enable UC's sales team?

  • A. Call Explorer
  • B. Call Summaries
  • C. Call Insights

Answer: C

Explanation:
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.


NEW QUESTION # 80
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The AI Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production.
Which other configuration steps should the AI Specialist take in the sandbox that can be deployed to the production org?

  • A. From the Epstein setup menu, select Turn on Einstein: create custom fields to store Issue, Resolution, and Summary: create a Quick Action that updates these fields: and add the wrap up componert to the Messaging session record page layout.
  • B. create custom fields to store Issue, Resolution, and Summary; create a Quick Action that updates these fields: add the Wrap Up component to the Messaging Session record paae layout: and create Permission Set Assignments for the intended Agents.
  • C. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: and ado the Wrap up component to the Messaging session record page lavcut.

Answer: C

Explanation:
* Context of the Question
* Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
* Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
* The AI Specialist needs to configureWork Summaries for Enhanced Messagingin the sandbox.
* What Can Actually Be Deployed from Sandbox to Production?
* Custom Fields: Metadata that is easily created in sandbox and then deployed.
* Quick Actions: Also metadata-based and can be deployed from sandbox to production.
* Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.
* Why Option C is Correct
* No Need to Turn on Einstein in Sandbox for Deployment: Einstein Generative AI is already enabled in Production; turning it on in the sandbox is typically a manual step if you want to test, but that step itself is not "deployable" in the sense of metadata.
* Permission Set Assignments(as in Option A) are not deployable metadata. You can deploy the Permission Set itself but not the specific user assignments. Since the question specifically asks
"Which other configuration steps should be takenin the sandboxthatcanbe deployed to the production org?", user assignment is not one of them.
* Why Not Option A or B?
* Option A: Mentions creating permission set assignments for agents. This cannot be directly deployed from sandbox to Production, as permission set assignments are user-specific and considered "data," not metadata.
* Option B: Mentions "Turn on Einstein." But Einstein Generative AI is already enabled in Production. Additionally, "Turning on Einstein" is typically an org-level setting, not a deployable metadata item.
* ConclusionThe main deployable items you can reliably create and test in a sandbox, and then migrate to Production, are:
* Custom Fields(Issue, Resolution, Summary).
* A Quick Actionthat updates those fields.
* Page Layout Changeto include the Wrap Up component.
Therefore,Option Cis correct and focuses on actions that are truly deployable as metadata from a sandbox to Production.
Salesforce AI Specialist References & Documents
* Salesforce Trailhead:Work Summaries with Einstein GPTProvides an overview of how to configure Work Summaries, including the need for custom fields, quick actions, and UI components.
* Salesforce Documentation:Deploying Metadata Between OrgsExplains what can and cannot be deployed via change sets (e.g., custom fields, page layouts, quick actions vs. user permission set assignments).
* Salesforce AI Specialist Study GuideOutlines which Einstein Generative AI and Work Summaries configurations are deployable as metadata.


NEW QUESTION # 81
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?

  • A. App Builder
  • B. Copilot Builder
  • C. Model Builder

Answer: C

Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce AI Specialist Reference:
For more details, check the Model Builder guide here: https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm


NEW QUESTION # 82
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 are limited to the same account.
  • C. Matched opportunities have a status of Closed Won from last 12 months.

Answer: C

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 # 83
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