認証トレーニングSalesforce-AI-Specialist試験問題集テストエンジン [2026]
2026年01月26日ガイド準備でSalesforce-AI-Specialist試験合格
Salesforce Salesforce-AI-Specialist 認定試験の出題範囲：
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質問 # 69
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. Dynamic Grounding
- B. Data Masking
- C. Prompt Defense
正解：A
解説：
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.
質問 # 70
Universal Containers (UC) wants to enable its sales reps to explore opportunities that are similar to previously won opportunities by entering the utterance, "Show me other opportunities like this one." How should UC achieve this in Einstein Copilot?
- A. Use the standard Copilot action.
- B. Create a custom Copilot action calling an Apex class.
- C. Create a custom Copilot action calling a flow.
正解：A
解説：
Universal Containers can achieve the request to explore similar opportunities by using thestandard Copilot action.Einstein Copilothas built-in actions to handle natural language queries, such as "Show me other opportunities like this one." The standard action will process the query and return results based on predefined matching criteria like opportunity details and past Closed Won deals.
This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box functionality.
For further details, refer toEinstein Copilot for Sales documentationregarding standard actions and natural language processing.
質問 # 71
Leadership needs to populate a dynamic form field with a summary or description created by a large language model (LLM) to facilitate more productive conversations with customers. Leadership also wants to keep a human in the loop to be considered in their AI strategy.
Which prompt template type should the AI Specialist recommend?
- A. Record Summary
- B. Sales Email
- C. Field Generation
正解：C
解説：
The correct answer is Field Generation because this template type is designed to dynamically populate form fields with content generated by a large language model (LLM). In this scenario, leadership wants a dynamic form field that contains a summary or description generated by AI to aid customer interactions. Additionally, they want to keep a human in the loop, meaning the generated content will likely be reviewed or edited by a person before it's finalized, which aligns with the Field Generation prompt template.
Field Generation: This prompt type allows you to generate content for specific fields in Salesforce, leveraging large language models to create dynamic and contextual information. It ensures that AI content is available within the record where needed, but it allows human oversight or review, supporting the "human-in-the-loop" strategy.
Sales Email: This prompt type is mainly used for generating email content for outreach or responses, which doesn't align directly with populating fields in a form.
Record Summary: While this option might seem close, it is typically used to summarize entire records for high-level insights rather than filling specific fields with dynamic content based on AI generation.
Salesforce AI Specialist Reference:
You can explore more about these prompt templates and AI capabilities through Salesforce documentation and official resources on Prompt Builder: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_templates_overview.htm
質問 # 72
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. Periodically change the templates grounding object.
- C. Use the Prompt Builder Scorecard to help monitor.
正解：C
解説：
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.
質問 # 73
Universal Containers is interested in using Call Explorer to quickly gain insights from meetings recorded by its sales team.
What should the AI Specialist be aware of before enabling this feature?
- A. Call Explorer requires the Einstein Conversation Insights permission set to be enabled.
- B. Custom Call Explorer actions need to be built before it can be configured.
- C. Call Explorer operates independently of Salesforce Knowledge, requiring no prior setup.
正解：A
解説：
Before enabling Call Explorer, the Salesforce AI Specialist must ensure that the Einstein Conversation Insights permission set is assigned to users (Option C). Call Explorer is a feature within Einstein Conversation Insights (ECI) that analyzes meeting recordings to surface trends, keywords, and actionable insights.
Key Considerations:
* Permission Set Requirement:
* Users (including admins) need the Einstein Conversation Insights permission set to access and use Call Explorer. Without this, the feature remains inaccessible.
* The permission set grants access to ECI tools, including call transcription, analysis, and dashboard visibility.
* Why Other Options Are Incorrect:
* A. Independence from Salesforce Knowledge: While Call Explorer does not rely on Salesforce Knowledge, this is irrelevant to the setup prerequisite. The critical dependency is the permission set, not Knowledge configuration.
* B. Custom Actions: Call Explorer does not require custom actions to be built before configuration. It is a pre-built analytics tool that works once permissions and data sources (e.g., call recordings) are configured.
References:
* Salesforce Einstein Conversation Insights Guide: Explicitly states that the Einstein Conversation Insights permission set is required to access Call Explorer.
* Trailhead Module: "Einstein Conversation Insights Basics" outlines permission prerequisites for enabling call analytics.
* Salesforce Help Documentation: Confirms that Call Explorer functionality is governed by ECI permissions.
質問 # 74
An AI Specialist at Universal Containers is working on a prompt template to generate personalized emails for product demonstrationrequests from customers. It is important for the Al-generated email to adhere strictly to the guidelines, using only associated opportunityinformation, 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.
正解：A
解説：
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 oftriple quotesallows 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 toSalesforce Prompt Builder documentationfor detailed instructions on how to structure prompts for generative AI.
質問 # 75
What is the main purpose of Prompt Builder?
- A. A tool for developers to use in Visual Studio Code that creates prompts for Apex programming, assisting developers in writing code more efficiently.
- B. A tool within Salesforce offering real-time Al-powered suggestions and guidance to users, Improving productivity and decision-making.
- C. A tool that enables companies to create reusable prompts for large language models (LLMs), bringing generative AI responses to their flow of work
正解：C
解説：
Prompt Builder is designed to help organizations create and configure reusable prompts for large language models (LLMs). By integrating generative AI responses into workflows, Prompt Builder enables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
* It is not a tool for Apex programming (as in option A).
* It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
References:
* Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
質問 # 76
Universal Containers (UC) is implementing generative AI and wants to leverage a prompt template to provide responses to customers that gives personalized product recommendations to website visitors based on their browsing history.
Which initial step should UC take to ensure the chatbot can deliver accurate recommendations'
- A. Collect and analyze browsing data.
- B. Write a response scrip for the chatbot.
- C. Design universal product recommendations.
正解：A
解説：
To enable personalized product recommendations using generative AI, the foundational step for Universal Containers (UC) is collecting and analyzing browsing data (Option C). Personalized recommendations depend on understanding user behavior, which requires structured data about their browsing history. Without this data, the AI model lacks the context needed to generate relevant suggestions.
* Data Collection: UC must first aggregate browsing data (e.g., pages visited, products viewed, session duration) to build a dataset that reflects user preferences.
* Data Analysis: Analyzing this data identifies patterns (e.g., frequently viewed categories) that inform how prompts should be structured to retrieve relevant recommendations.
* Grounding in Data: Salesforce's Prompt Templates rely on grounding data to generate accurate outputs. Without analyzing browsing data, the prompt template cannot reference meaningful insights for personalization.
Options A and D are incorrect because:
* Universal recommendations (A) ignore personalization, which is the core requirement.
* Writing a response script (D) addresses chatbot interaction design, not the accuracy of recommendations.
References:
* Salesforce AI Specialist Certification Guide: Highlights the importance of grounding prompts in relevant data sources to ensure accuracy.
* Trailhead Module: "Einstein for Developers" emphasizes data preparation as a prerequisite for effective AI-driven personalization.
* Salesforce Help Documentation: Recommends analyzing user behavior data to tailor generative AI outputs in commerce use cases.
質問 # 77
An AI Specialist is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends.
What functionality allows the AX Specialist to achieve this?
- A. AI Audit & Feedback Data dashboard
- B. User Utterances dashboard
- C. Agent Event Logs dashboard
正解：B
解説：
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the AI Specialist to:
* Identify Common Queries: Surface frequently asked questions or recurring issues.
* Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
* Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.
Why Other Options Are Incorrect:
* B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.
* C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.
References:
* Salesforce Einstein AI Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.
* Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.
* Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.
質問 # 78
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 support the Delete Record action.
- C. Einstein Copilot does not have the permission to delete the user's records.
正解：B
解説：
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.
Reference:
Salesforce AI Specialist Documentation - Einstein Copilot Supported Actions:
Lists the actions that Einstein Copilot can perform, noting the absence of delete operations.
Salesforce Help - Limitations of Einstein Copilot:
Highlights current limitations, including unsupported actions like deleting records.
質問 # 79
An account manager is preparing for an upcoming customer call and wishes to get a snapshot of key data points from accounts, contacts, leads, and opportunities in Salesforce.
Which feature provides this?
- A. Sales Insight Summary
- B. Sales Summaries
- C. Work Summaries
正解：A
解説：
Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads, opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.
* Option A (Sales Summaries): Typically refers to Einstein-generated summaries of specific interactions (e.g., emails, calls), not multi-object snapshots.
* Option C (Work Summaries): Focuses on summarizing customer service interactions (e.g., chat transcripts), not sales data.
* Option B (Sales Insight Summary): Directly provides a holistic snapshot of sales-related objects, aligning with the scenario.
References:
* Salesforce Help: Sales Insight Overview
* Describes Sales Insight Summary as "a unified view of account, contact, and opportunity data for sales readiness."
質問 # 80
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should an AI Specialist recommend to resolve the issue?
- A. Enable data masking for sandbox refreshes.
- B. Configure data masking in the Einstein Trust Layer setup.
- C. Add new data masking rules in LLM setup.
正解：B
解説：
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the AI Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce AI Specialist References:For more information, refer to: https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer_data_masking.htm
質問 # 81
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.
正解：C
解説：
To monitorEinstein Copilot'susability and the assignment of actions, the AI Specialist should runEinstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types ofactions 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 Logsare not relevant for tracking user behavior or the assignment of Copilot actions.
* Querying the Copilot log data via the Metadata APIwould not provide the necessary insights in a structured manner.
For more details, refer toSalesforce's Copilot Analytics documentationfor tracking AI-driven interactions.
質問 # 82
Universal Containers wants to reduce overall agent handling time minimizing the time spent typing routine answers for common questionsin-chat, and reducing the post-chat analysis by suggesting values for case fields.
Which combination of Einstein for Service features enables this effort?
- A. Einstein Reply Recommendations and Case Classification
- B. Einstein Service Replies and Work Summaries
- C. Einstein Reply Recommendations and Case Summaries
正解：A
解説：
Universal Containers aims to reduce overall agent handling time by minimizing the time agents spend typing routine answers for common questions during chats and by reducing post-chat analysis through suggesting values for case fields.
To achieve these objectives, the combination ofEinstein Reply RecommendationsandCase Classificationis the most appropriate solution.
1. Einstein Reply Recommendations:
* Purpose:Helps agents respond faster during live chats by suggesting the best responses based on historical chat data and common customer inquiries.
* Functionality:
* Real-Time Suggestions:Provides agents with a list of recommended replies during a chat session, allowing them to quickly select the most appropriate response without typing it out manually.
* Customization:Administrators can configure and train the model to ensure the recommendations are relevant and accurate.
* Benefit:Significantly reduces the time agents spend typing routine answers, thus improving efficiency and reducing handling time.
2. Case Classification:
* Purpose:Automatically suggests or populates values for case fields based on historical data and patterns identified by AI.
* Functionality:
* Field Predictions:Predicts values for picklist fields, checkbox fields, and more when a new case is created.
* Automation:Can be set to auto-populate fields or provide suggestions for agents to approve.
* Benefit:Reduces the time agents spend on post-chat analysis and data entry by automating the classification and field population process.
Why Options A and B are Less Suitable:
* Option A (Einstein Service Replies and Work Summaries):
* Einstein Service Replies:Similar to Reply Recommendations but typically used for email and not live chat.
* Work Summaries:Provides summaries of customer interactions but does not assist in field value suggestions.
* Option B (Einstein Reply Recommendations and Case Summaries):
* Case Summaries:Generates a summary of the case details but does not help in suggesting field values.
References:
* Salesforce AI Specialist Documentation -Einstein Reply Recommendations:
* Details how Reply Recommendations assist agents in providing quick responses during live chats.
* Salesforce AI Specialist Documentation -Einstein Case Classification:
* Explains how Case Classification predicts and suggests field values to streamline case management.
* Salesforce Trailhead -Optimize Service with AI:
* Provides an overview of AI features that enhance service efficiency.
質問 # 83
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization.
What should the AI Specialist recommend?
- A. Agentforce Analytics
- B. Agent Studio Analytics
- C. Agent Analytics
正解：C
解説：
* Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.
* Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.
* Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus,Agent Analyticsis the correct recommendation as it offers actionable insights to drive agent adoption and productivity.
質問 # 84
What is an appropriate use case for leveraging Agentforce Sales Agent in a sales context?
- A. Enable a sales team by providing them with an interactive step-by-step guide based on business rules to ensure accurate data entry into Salesforce and help close deals fatter.
- B. Instantly review and read incoming messages or emails that are then logged to the correct opportunity, contact, and account records to provide a full view of customer interactions and communications.
- C. Enable a sates team to use natural language to invoke defined sales tasks grounded in relevant data and be able to ensure company policies are applied. conversationally and in the now or work.
正解：C
解説：
Agentforce Sales Agent is designed to let sales teams perform tasks via natural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to "update the opportunity stage to Closed Won" or "generate a quote," with the system enforcing validations and data security. This use case aligns with Salesforce's vision of conversational AI streamlining workflows without compromising compliance.
* Step-by-step guides (B) are typically handled by tools like Dynamic Forms or Guided Selling, not Agentforce.
* Logging messages/emails (C) is managed by Email-to-Case or Service Cloud, not a sales-specific AI agent.
質問 # 85
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.
正解：C
解説：
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.
質問 # 86
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. Model Builder
- C. Copilot Builder
正解：B
解説：
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
質問 # 87
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