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Salesforce-AI-Specialist試験問題解説集、Salesforce-AI-Specialistテスト内容
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Salesforce Salesforce-AI-Specialist 認定試験の出題範囲：
| トピック |
出題範囲 |
| トピック 1 |
- 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.
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| トピック 2 |
- 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.
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| トピック 3 |
- 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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| トピック 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.
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| トピック 5 |
- 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.
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>> Salesforce-AI-Specialist試験問題解説集 <<
Salesforce-AI-Specialistテスト内容 & Salesforce-AI-Specialist最新受験攻略
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Salesforce Certified AI Specialist Exam 認定 Salesforce-AI-Specialist 試験問題 (Q147-Q152):
質問 # 147
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
- A. Generative model
- B. Predictive model and generative model
- C. Predictive model
正解：C
解説：
Universal Containersshould use aPredictive modelto decide which of the three emails to send based on the customer'slifetime value scoreandmarket segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provideexplainabilityto show why a certain email was chosen, which is crucial for UC' s requirement to explain the decision-making process.
* Generative modelsare typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
* Predictive modelsoffer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer toSalesforce's Predictive AI model documentationfor more insights on how predictive models are used for segmentation and decision making.
質問 # 148
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 Insights
- C. Call Summaries
正解：B
解説：
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.
質問 # 149
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?
- A. Service AI Grounding visibility works m system mode.
- B. Service AI Grounding only supports String and Text Area type fields.
- C. Service AI Grounding only works with Case and Knowledge objects.
正解：B
解説：
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses. Key considerations:
* Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
* Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
* Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect).
References:
* Salesforce Help: Service AI Grounding Requirements
* Explicitly states support for "Text Area and String fields" only.
質問 # 150
Universal Containers (UC) is looking to improve its sales team's productivity by providing real-time insights and recommendations during customer interactions.
Why should UC consider using Agentforce Sales Agent?
- A. To streamline the sales process and increase conversion rates
- B. To automate the entire sales process for maximum efficiency
- C. To track customer interactions for future analysis
正解：A
解説：
Agentforce Sales Agent provides real-time insights and AI-powered recommendations, which are designed to streamline the sales processand help sales representatives focus on key tasks toincrease conversion rates. It offers features like lead scoring, opportunity prioritization, and proactive recommendations, ensuring that sales teams can interact with customers efficiently and close deals faster.
* Option A: While tracking customer interactions is beneficial, it is only part of the broader capabilities offered by Agentforce Sales Agent and is not the primary objective for improving real-time productivity.
* Option B: Agentforce Sales Agent does not automate the entire sales process but provides actionable recommendations to assist the sales team.
* Option C: This aligns with the tool's core purpose of enhancing productivity and driving sales success.
質問 # 151
A data science team has trained an XGBoost classification model for product recommendations onDatabricks.
The AI Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the AI Specialist set this up?
- A. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
- B. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
- C. Create the serving endpoint in Databricks, then configure the model using Model Builder.
正解：C
解説：
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, the AI Specialist should configure it within Einstein Studio'sModel Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
質問 # 152
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