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Salesforce Certified Agentforce Specialist Sample Questions (Q143-Q148):

NEW QUESTION # 143
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?

A. Prompt Defense
B. Secure Data Retrieval and Grounding
C. Data Masking

Answer: A

Explanation:
The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.
Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.
In contrast:
* Toxicity Scoring (A) identifies harmful or inappropriate content in outputs but does not address data privacy.
* Prompt Defense (C) guards against malicious prompts or injection attacks but focuses on security rather than data protection compliance.
Reference:
Salesforce Help Article: Einstein Trust Layer ("Data Masking" section).
Einstein Trust Layer Overview: "Data Protection and Compliance Features" (GDPR alignment via Data Masking).

NEW QUESTION # 144
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?

A. Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.
B. Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.
C. Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let's analyze their roles.
* Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.In a Knowledge-based data library,identifying fields(e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context.Content fields(e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce's documentation on Knowledge integration, making it the correct answer.
* Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.Identifying fields do more than categorize-they actively locate articles, not just index them. Content fields aren't limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
* Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:
The primary difference-identifying fields for locating articles and content fields for enriching responses- reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
References:
Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup- Defines identifying vs. content fields.
Trailhead: Ground Your Agentforce Prompts- Explains field roles in Knowledge integration.
Salesforce Help: Knowledge in Agentforce- Confirms locating and enriching functions.

NEW QUESTION # 145
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. Design universal product recommendations.
B. Write a response scrip for the chatbot.
C. Collect and analyze browsing data.

Answer: C

Explanation:
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:
* SalesforceAgentforce SpecialistCertification 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.

NEW QUESTION # 146
Universal Containers is rolling out a new generative AI initiative.
Which Prompt Builder limitations should theAgentforce Specialistbe aware of?

A. Creations or updates to the prompt templates are not recorded in the Setup Audit Trail.
B. Rich text area fields are only supported in Flex template types.
C. Custom objects are supported only for Flex template types.

Answer: C

Explanation:
ThePrompt Builderin Salesforce has some specific limitations, one of which is thatcustom objectsare supportedonly for Flex template types. This means that users must rely on Flex templates to integrate custom objects into their prompts.
* Option A: While rich text area fields have certain restrictions, this does not pertain to the core limitation of integrating custom objects.
* Option B: Updates and creations for prompt templates are indeed recorded in the Setup Audit Trail, so this statement is incorrect.
* Option C: This is the correct answer as it reflects a documented limitation of the Prompt Builder.

NEW QUESTION # 147
What is true of Agentforce Testing Center?

A. Running tests does not consume Einstein Requests.
B. Running tests risks modifying CRM data in a production environment.
C. Agentforce Testing Center can only be used in a production environment.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The Agentforce Testing Center is a tool in Agentforce Studio for validating agent performance. Let's evaluate the statements.
* Option A: Running tests risks modifying CRM data in a production environment.Agentforce Testing Center runs synthetic interactions in a controlled environment (e.g., sandbox or isolated test space) and doesn't modify live CRM data. It's designed for safe pre-deployment testing, making this incorrect.
* Option B: Running tests does not consume Einstein Requests.Einstein Requests are part of the usage quota for Einstein Generative AI features (e.g., prompt executions in production). Testing Center uses synthetic data to simulate interactions without invoking live AI calls that count against this quota.
Salesforce documentation confirms tests don't consume requests, making this the correct answer.
* Option C: Agentforce Testing Center can only be used in a production environment.Testing Center is available in both sandbox and production orgs, but it's primarily used pre-deployment (e.g., in sandboxes) to validate agents safely. This restriction is false, making it incorrect.
Why Option B is Correct:
Not consuming Einstein Requests is a key feature of Testing Center, allowing extensive testing without impacting quotas, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Testing Center > Overview- Confirms no request consumption.
Trailhead: Test Your Agentforce Agents- Notes quota-free testing.
Salesforce Help: Agentforce Testing- Details safe, isolated testing.

NEW QUESTION # 148
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