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| Section | Weight | Objectives |
|---|---|---|
| Testing, Deployment, and Operations | 20% | - Monitoring
|
| AI Agents | 35% | - Security and governance
|
| Prompt Engineering | 25% | - Grounding and best practices
|
| Data and AI Governance | 20% | - Data grounding
|
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NEW QUESTION # 116
The sales team at a hotel resort would like to generate a guest summary about the guests' interests and provide recommendations based on their activity preferences captured in each guest profile. They want the summary to be available only on the contact record page. Which AI capability should the team use?
Answer: C
Explanation:
The hotel resort team needs an AI-generated guest summary with recommendations, displayed exclusively on the contact record page. Let's assess the options.
Option A: Flow Model BuilderModel Builder in Salesforce creates custom predictive AI models (e.g., for scoring or classification) using Data Cloud or Einstein Platform data. It's not designed for generating text summaries or embedding them on record pages, making it incorrect.
Option B: Agentforce BuilderAgent Builder in Agentforce Studio creates autonomous AI agents for tasks like lead qualification or customer service. While agents can provide summaries, they operate in conversational interfaces (e.g., chat), not as static content on a record page. This doesn't meet the location-specific requirement, making it incorrect.
Option C: Prompt BuilderEinstein Prompt Builder allows creation of prompt templates that generate text (e.g., summaries, recommendations) using Generative AI. The template can pull data from contact records (e.g., activity preferences) and be embedded as a Lightning component on the contact record page via a Flow or Lightning App Builder. This ensures the summary is available only where specified, meeting the team's needs perfectly and making it the correct answer.
Why Option C is Correct:
Prompt Builder's ability to generate contextual summaries and integrate them into specific record pages via Lightning components aligns with the team's requirements, as supported by Salesforce documentation.
References:
Salesforce Agentforce Documentation: Prompt Builder > Embedding Prompts - Details placement on record pages.
Trailhead: Build Prompt Templates in Agentforce - Covers summaries from object data.
Salesforce Help: Customize Record Pages with AI - Confirms Prompt Builder integration.
NEW QUESTION # 117
Universal Containers (UC) is discussing its AI strategy in an agile Scrum meeting.
Which business requirement would lead An Agentforce to recommend connecting to an external foundational model via Einstein Studio (Model Builder)?
Answer: A
Explanation:
Einstein Studio (Model Builder) allows organizations to connect and utilize external foundational models while fine-tuning them with company-specific data. This capability is particularly suited to businesses like Universal Containers (UC) that require customization of foundational models to better align with their unique data and use cases.
* Option A: Adjusting model temperature is a parameter-level setting for controlling randomness in AI- generated responses but does not necessitate connecting to an external foundational model.
* Option B: This is the correct answer because Einstein Studio supports fine-tuning external models with proprietary company data, enabling a tailored and more accurate AI solution for UC.
* Option C: Changing frequency penalties is another parameter-level adjustment and does not require external foundational models or Einstein Studio.
NEW QUESTION # 118
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they ' ve written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
Answer: A
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs: Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
Option B: The " Response " output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response-it includes the entire payload sent to the Trust Layer.
Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate " which sensitive data is masked. " Instead, it shows the full text, including any masked portions, as processed by the Trust Layer-not a separate masking log.
Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.
Salesforce Agentforce Documentation: " Preview Prompt Templates " (Salesforce Help: https://help.
salesforce.com/s/articleView?id=sf.agentforce_prompt_preview.htm & type=5) Salesforce Einstein Trust Layer Documentation: " Trust Layer Outputs " (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm & type=5)
NEW QUESTION # 119
The VP of Service at Universal Containers requires a report showing weekly deflection rate trends and escalation volumes for the Agentforce Service Agent over the past 90 days.
Which approach should the Agentforce Specialist recommend?
Answer: B
Explanation:
The correct answer is B because the requirement is a standard operational report for Service Agent performance over time. Agent Analytics in Agentforce Observability is the built-in analytics layer for tracking agent usage, effectiveness, deflection, escalation, and abandonment behavior. It is the most direct and maintainable approach for a VP-level service dashboard. Option A is not the best fit because health monitoring and alert thresholds are not the primary reporting mechanism for historical weekly trend analysis. Option C is unnecessarily custom for a standard metric request and should be reserved only when prebuilt analytics do not satisfy the reporting need. Salesforce documentation states that Agentforce Analytics uses Data 360 reports to provide insight into agent performance, and Observability includes effectiveness metrics such as escalation and deflection.
NEW QUESTION # 120
An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account.
Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?
Answer: A
Explanation:
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1 Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta/salesforce_ai
/grounding_data_prompts
NEW QUESTION # 121
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