Master Salesforce Agentforce-Specialist Exam with Reliable Practice Questions
Upgrade to PremiumUniversal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
In the score of 1 indicate?
Correct : C
Einstein Trust Layer'sToxicity Scoringcategorizes content on a scale of0 to 1, where1 indicates the highest level of toxicity(e.g., harmful, biased, or inappropriate language). This scoring helps organizations filter unsafe AI-generated content. A score of 1 triggers mitigation actions, such as blocking the response or alerting administrators.
A score of 0would indicate no toxicity (B is incorrect).
The scoring system does not use 'least toxic' as a category (A is misleading).
Einstein GPT Safety Overview: 'Mitigating Harmful Content with Toxicity Detection.'
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An AI Specialist at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. 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?
Correct : C
ForField Generationprompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on theLightning 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.
Reactivating the layout(A) is unnecessary unless the layout itself was modified after activation.
Case objects are supportedfor Field Generation (B is incorrect).
Einstein GPT Implementation Guide: 'Enabling AI-Generated Fields in Lightning Pages.'
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An AI Specialist wants to troubleshoot their Agent's performance.
Where should the AI Specialist go to access all user interactions with the Agent, including Agent erro|rs, incorrectly triggered actions, and incomplete plans?
Correct : B
Event Logsin Salesforce capture detailed interaction data, including agent errors, triggered actions, and incomplete plans. These logs provide visibility into user-Agent interactions for troubleshooting performance issues. TheEinstein Bot Analyticsdocumentation highlights Event Logs as the primary source for auditing bot behavior and diagnosing issues like misconfigured actions or plan execution failures.
Plan Canvas(B) is for designing workflows, not auditing.
Agent Settings(C) control configuration but do not store interaction history.
Salesforce Help Article:Einstein Bot Analytics and Logs('Accessing Event Logs' section).
Einstein Bot Developer Guide: 'Monitoring and Debugging Agent Performance.'
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What is an appropriate use case for leveraging Agentforce Sales Agent in a sales context?
Correct : A
Agentforce Sales Agentis designed to let sales teams perform tasks vianatural 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.
Salesforce Help Article:Agentforce for Sales('Use Cases and Capabilities' section).
Einstein AI Specialist Trailhead: 'Sales Automation with Agentforce' (Natural Language Task Execution).
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An AI Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the AI Specialist recommend to achieve this use case?
Correct : A
For no-code implementations,Prompt Builderallows AI Specialists to createprompt templatesthat dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine-tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
Salesforce Help Article:Prompt Builder for No-Code AI('Grounding in CRM and Data Cloud' section).
Einstein GPT Implementation Guide: 'Generating Summaries with Pre-Built Models.'
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