ACE SALESFORCE AGENTFORCE-SPECIALIST EXAM INSTANTLY WITH THIS TRIED-AND-TESTED METHOD

Ace Salesforce Agentforce-Specialist Exam Instantly with This Tried-and-Tested Method

Ace Salesforce Agentforce-Specialist Exam Instantly with This Tried-and-Tested Method

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Tags: Agentforce-Specialist Free Exam, Agentforce-Specialist Valid Test Question, Authorized Agentforce-Specialist Pdf, Agentforce-Specialist Instant Access, Certification Agentforce-Specialist Sample Questions

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Everything needs a right way. The good method can bring the result with half the effort, the same different exam also needs the good test method. Our Agentforce-Specialist study materials in every year are summarized based on the test purpose, every answer is a template, there are subjective and objective exams of two parts, we have in the corresponding modules for different topic of deliberate practice. To this end, our Agentforce-Specialist Study Materials in the qualification exam summarize some problem- solving skills, and induce some generic templates.

Salesforce Certified Agentforce Specialist Sample Questions (Q169-Q174):

NEW QUESTION # 169
An Agentforce Specialist needs to create a prompt template to fill a custom field named Latest Opportunities Summary on the Account object with information from the three most recently opened opportunities. How should the Agentforce Specialist gather the necessary data for the prompt template?

  • A. Select the Account Opportunity object as a resource when creating the prompt template.
  • B. Select the latest Opportunities related list as a merge field.
  • C. Create a flow to retrieve the opportunity information.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using aFlow(Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.
* Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don't natively support querying or aggregating related list data like the three most recent opportunities.
* Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don't directly query related objects without additional logic (e.g., a Flow), making this incorrect.
* Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.
Thus, Option B is the correct method to gather the necessary data efficiently and accurately.
References:
* Salesforce Agentforce Documentation: "Integrate Flows with Prompt Templates" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.agentforce_flow_prompt_integration.htm&type=5)
* Trailhead: "Build Flows for Agentforce"(https://trailhead.salesforce.com/content/learn/modules/flows- for-agentforce)


NEW QUESTION # 170
Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft emailcontains placeholders for attributes that could have been derived from the recipient's contact record. What is the most likely explanation for why the draft email shows these placeholders?

  • A. The user does not have permission to access the fields.
  • B. The user does not have Einstein Sales Emails permission assigned.
  • C. The user's locale language is not supported by Prompt Builder.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC is using an Einstein Generative AI feature (likely Einstein Sales Emails) to draft personalized emails, but placeholders (e.g., {!Contact.FirstName}) appear instead of actual data from the contact record. Let's analyze the options.
* Option A: The user does not have permission to access the fields.Einstein Sales Emails, built on Prompt Builder, pulls data from contact records to populate email drafts. If the user lacks field-level security (FLS) or object-level permissions to access relevant fields (e.g., FirstName, Email), the system cannot retrieve the data, leaving placeholders unresolved. This is a common issue in Salesforce when permissions restrict data access, making it the most likely explanation and the correct answer.
* Option B: The user's locale language is not supported by Prompt Builder.Prompt Builder and Einstein Sales Emails support multiple languages, and locale mismatches typically affect formatting or translation, not data retrieval. Placeholders appearing instead of data isn't a documented symptom of language support issues, making this unlikely and incorrect.
* Option C: The user does not have Einstein Sales Emails permission assigned.The Einstein Sales Emails permission (part of the Einstein Generative AI license) enables the feature itself. If missing, users couldn't generate drafts at all-not just see placeholders. Since drafts are being created, this permission is likely assigned, making this incorrect.
Why Option A is Correct:Permission restrictions are a frequent cause of unresolved placeholders in Salesforce AI features, as the system respects FLS and sharing rules. This is well-documented in troubleshooting guides for Einstein Generative AI.
References:
* Salesforce Help: Einstein Sales Emails > Troubleshooting- Lists permissions as a cause of data issues.
* Trailhead: Set Up Einstein Generative AI- Emphasizes field access for personalization.
* Agentforce Documentation: Prompt Builder > Data Access- Notes dependency on user permissions.


NEW QUESTION # 171
When creating a custom retriever in Einstein Studio, which step is considered essential?

  • A. Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.
  • B. Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.
  • C. Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce's Einstein Studio (part of the Agentforce ecosystem), creating acustom retrieverinvolves setting up a mechanism to fetch data for AI prompts or responses. Theessential stepis defining the foundation of the retriever: selecting thesearch index, specifying thedata model object (DMO), and identifying thedata space(Option A). These elements establish where and what the retriever searches:
* Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
* Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
* Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the data.
Filters are noted as optional in Option A, which is accurate-they enhance precision but aren't mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
* Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever's output, but it's a secondary step. The retriever must first know where to search (A) before output can be configured.
* Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
* Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
References:
* Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
* Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/einstein-studio-for-agentforce)


NEW QUESTION # 172
In Model Playground, which hyperparameters of an existing
Salesforce-enabled foundational model can An Agentforce change?

  • A. Temperature, Frequency Penalty, Presence Penalty
  • B. Temperature, Frequency Penalty, Output Tokens
  • C. Temperature, Top-k sampling, Presence Penalty

Answer: A

Explanation:
InModel Playground, An Agentforce working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
* Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
* Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
* Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer toSalesforce AI Model Playgroundguidance from Salesforce's official documentation on foundational model adjustments.


NEW QUESTION # 173
Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should theAgentforce Specialistrecommend?

  • A. Reply Recommendations and Sales Summaries
  • B. Service Replies and Work Summaries
  • C. Service Replies and Case Summaries

Answer: C


NEW QUESTION # 174
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Everything needs a right way. The good method can bring the result with half the effort, the same different exam also needs the good test method. Our Agentforce-Specialist study materials in every year are summarized based on the test purpose, every answer is a template, there are subjective and objective exams of two parts, we have in the corresponding modules for different topic of deliberate practice. To this end, our Agentforce-Specialist Study Materials in the qualification exam summarize some problem- solving skills, and induce some generic templates.

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