Free Salesforce Salesforce-AI-Specialist Practice Exam with Questions & Answers | Set: 3

Knowledge Replies is the Einstein Generative AI capability that generates recommended responses for service agents by grounding responses in the organization’s Knowledge articles. This reduces handling time by providing contextually accurate suggestions sourced directly from approved content. Salesforce documentation states that Knowledge Replies leverage natural language processing (NLP) to match customer inquiries with relevant articles and draft replies, ensuring consistency and compliance.

Case Replies (A) focus on generating responses based on case data (e.g., case fields, history) but do not explicitly ground responses in Knowledge articles.

Service Replies (C) is not a standard Einstein capability tied to Knowledge-driven responses.

Dynamic grounding with secure data retrieval is a key feature in Salesforce'sEinstein Trust Layer, which provides enhanced data protection and ensures that AI-generated outputs are both accurate and securely sourced. This feature allowsrelevant Salesforce datato be merged into the AI-generated responses, ensuring that the AI outputs are contextually aware and aligned with real-time CRM data.

Dynamic grounding means that AI models are dynamically retrieving relevant information from Salesforce records (such as customer records, case data, or custom object data) in a secure manner. This ensures that any sensitive data is protected during AI processing and that the AI model’s outputs are trustworthy and reliable for business use.

The other options are less aligned with the requirement:

Data maskingrefers to obscuring sensitive data for privacy purposes and is not related to merging Salesforce records into prompts.

Zero-data retention policyensures that AI processes do not store any user data after processing, but this does not address the need to merge Salesforce record information into a prompt.

References:

Salesforce Developer Documentation onEinstein Trust Layer

Salesforce Security Documentation for AI andData Privacy

To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the AI Specialist should create aTrust Layer audit reportwithinData Cloud. By using thetoxicity detector type filter, the report can displaytoxic responsesalong with their respective toxicity scores, allowingUniversal Containersto monitor and manage any toxic content generated with a high level of confidence.

Option Cis correct because it enables visibility into toxic language detection within theTrust Layerand allows for auditing responses for toxicity.

Option Asuggests checking a toxicity detection log, butSalesforceprovides more comprehensive options via the audit report.

Option Binvolves creating a flow, which is unnecessary for toxicity detection monitoring.

References:

Salesforce Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm

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.

To use the Record Snapshots grounding feature in a prompt template, you must create a field set that includes all fields required for grounding. Field sets define which fields from an object are accessible to the AI model, ensuring the prompt template has structured data to generate contextually accurate responses. Salesforce documentation emphasizes that grounding relies on explicitly defined field sets to avoid exposing unintended data and to comply with security policies.

Page Layout configuration (A) controls UI organization but does not directly enable grounding.

Dynamic Forms (C) customize record pages dynamically but are unrelated to data grounding for prompts.

When creating acustom actionin Einstein Copilot, one of the available options is to useFlows. Flows are a powerful automation tool in Salesforce, allowing the AI Specialist to define custom logic and actions within the Copilot system. This makes it easy to extend Copilot's functionality without needing custom code.

WhileApex triggersandSOQLare important Salesforce tools,Flowsare the recommended method for creating custom actions within Einstein Copilot because they are declarative and highly adaptable.

For further guidance, refer toSalesforce Flow documentationandEinstein Copilot customization resources.

Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is aFlex prompt template.

AFlex prompt templateis designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.

In contrast:

ARecord summary prompt template(Option A) is used to summarize data related to a specific record, such as generating a quick summary of a sales opportunity or account, but not for recommending products.

AnEmail generation prompt template(Option B) is tailored for crafting email content and is not suitable for suggesting products based on a catalog.

Given the need for dynamic recommendations that pull from a product catalog and potentially other sales data, theFlex prompt templateis the correct approach.

Salesforce References:

Salesforce Prompt Templates Overview:https://help.salesforce.com/s/articleView?id=000391407 &type=1

Flex Prompt Template Usage:https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta/salesforce_ai/prompt_flex_template

When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.

Salesforce documentation emphasizes that Action Instructions directly impact the bot’s ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot’s behavior with business requirements.

In contrast:

Action Name (A) is a static identifier and does not affect functionality.

Action Input (B) defines parameters passed to the action but does not dictate execution logic.

Thus, iterating on Action Instructions (C) ensures the action performs as expected.

Thestandard Einstein Copilot actionthat would be used in response to the user utterance, “Show me all the customers in New York,” isQuery Records. This action is responsible for retrieving a set of records from Salesforce based on a specified condition — in this case, filtering customers by location (New York).

Query Recordsis the action that fetches relevant data based on the criteria provided in the user's input.

Select Recordsis more about picking specific records from an already presented list.

Fetch Recordsis not a standard term used in this context for the action.

Refer toEinstein Copilot documentationon how Copilot actions work with natural language queries and data retrieval.

To create a digest of account action plans using the generative API feature,Universal Containersshould use theREST API. TheREST APIis ideal for integrating Salesforce with external systems and enabling interaction with Salesforce data, including generative capabilities like creating summaries or digests. It supports modern web standards and is suitable for flexible, lightweight interactions between Salesforce and legacy systems.

Metadata APIis used for retrieving and deploying metadata, not for data operations like generating summaries.

SOAP APIis an older API used for integration but is less flexible compared to REST for this specific use case.

For more details, refer toSalesforce REST API documentationregarding using REST for data integration and generating content.