TRUSTED AGENTFORCE-SPECIALIST DUMP COLLECTION & GUARANTEED SALESFORCE AGENTFORCE-SPECIALIST EXAM SUCCESS WITH VALID AGENTFORCE-SPECIALIST MINIMUM PASS SCORE

Trusted Agentforce-Specialist Dump Collection & Guaranteed Salesforce Agentforce-Specialist Exam Success with Valid Agentforce-Specialist Minimum Pass Score

Trusted Agentforce-Specialist Dump Collection & Guaranteed Salesforce Agentforce-Specialist Exam Success with Valid Agentforce-Specialist Minimum Pass Score

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Salesforce Agentforce-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.
Topic 2
  • Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.
Topic 3
  • Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.
Topic 4
  • Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.
Topic 5
  • Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.

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Salesforce Certified Agentforce Specialist Sample Questions (Q106-Q111):

NEW QUESTION # 106
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?

  • A. Predictive model
  • B. Generative model
  • C. Predictive model and generative model

Answer: A

Explanation:
Universal Containersshould use aPredictive modelto decide which of the three emails to send based on the customer'slifetime value scoreandmarket segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provideexplainabilityto show why a certain email was chosen, which is crucial for UC' s requirement to explain the decision-making process.
* Generative modelsare typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
* Predictive modelsoffer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer toSalesforce's Predictive AI model documentationfor more insights on how predictive models are used for segmentation and decision making.


NEW QUESTION # 107
In the context of retriever and search indexes, what best describes the data preparation process in Data Cloud?

  • A. Data preparation Involves loading, chunking, vectorizing, and storing content in a search-optimized manner to support retrieval from the vector database.
  • B. Data preparation focuses on real-time data ingestion and dynamic indexing to generate dynamic grounding reference data without preprocessing steps.
  • C. Data preparation entails aggregating, normalizing, and encoding structured datasets to ensure compliance with data governance and security protocols.

Answer: A

Explanation:
Why is "Loading, Chunking, Vectorizing, and Storing" the correct answer?
Agentforce AI-powered search and retriever indexing requires data to be structured and optimized for retrieval. The Data Cloud preparation process involves:
Key Steps in the Data Preparation Process for Agentforce:
* Loading Data
* Raw text from documents, emails, chat transcripts, and Knowledge articles is loaded into Data Cloud.
* Chunking (Breaking Text into Small Parts)
* AI divides long-form text into retrievable chunks to improve response accuracy.
* Example: A 1000-word article might be split into multiple indexed paragraphs.
* Vectorization (Transforming Text for AI Retrieval)
* Each text chunk is converted into numerical vector embeddings.
* This enables faster AI-powered searches based on semantic meaning, not just keywords.
* Storing in a Vector Database
* The processed data is stored in a search-optimized vector format.
* Agentforce AI retrievers use this data to find relevant responses quickly.
Why Not the Other Options?
# A. Real-time data ingestion and dynamic indexing
* Incorrect because while real-time updates can occur, the primary process involves preprocessing and indexing first.
# B. Aggregating, normalizing, and encoding structured datasets
* Incorrect because this process relates to data compliance and security, not AI retrieval optimization.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that data preparation includes chunking, vectorizing, and storing for AI retrieval in Data Cloud.


NEW QUESTION # 108
Which scenario best demonstrates when an Agentforce Data Library is most useful for improving an AI agent' s response accuracy?

  • A. When the AI agent needs to combine data from disparate sources based on mutually common data, such as Customer Id and Product Id for grounding.
  • B. When the AI agent must provide answers based on a curated set of policy documents that are stored, regularly updated, and indexed in the data library.
  • C. When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:The Agentforce Data Library enhances AI accuracy by grounding responses in curated, indexed data. Let's assess the scenarios.
* Option A: When the AI agent must provide answers based on a curated set of policy documents that are stored, regularly updated, and indexed in the data library.The Data Library is designed to store and index structured content (e.g., Knowledge articles, policy documents) for semantic search and grounding. It excels when an agent needs accurate, up-to-date responses from a managed corpus, like policy documents, ensuring relevance and reducing hallucinations. This is a prime use case per Salesforce documentation, making it the correct answer.
* Option B: When the AI agent needs to combine data from disparate sources based on mutually common data, such as Customer Id and Product Id for grounding.Combining disparate sources is more suited to Data Cloud's ingestion and harmonization capabilities, not the Data Library, which focuses on indexed content retrieval. This scenario is less aligned, making it incorrect.
* Option C: When data is being retrieved from Snowflake using zero-copy for vectorization and retrieval.Zero-copy integration with Snowflake is a Data Cloud feature, but the Data Library isn't specifically tied to this process-it's about indexed libraries, not direct external retrieval. This is a different context, making it incorrect.
Why Option A is Correct:The Data Library shines in curated, indexed content scenarios like policy documents, improving agent accuracy, as per Salesforce guidelines.
References:
* Salesforce Agentforce Documentation: Data Library > Use Cases- Highlights curated content grounding.
* Trailhead: Ground Your Agentforce Prompts- Describes Data Library accuracy benefits.
* Salesforce Help: Agentforce Data Library- Confirms policy document scenario.


NEW QUESTION # 109
An Agentforce 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 Agentforce Specialist recommend to achieve this use case?

  • A. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.
  • B. Fine-Tune the standard foundational model due to the complexity of the data.
  • C. Deploy UC's own custom foundational model on this data first.

Answer: A

Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that 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.


NEW QUESTION # 110
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should An Agentforce recommend to resolve the issue?

  • A. Configure data masking in the Einstein Trust Layer setup.
  • B. Add new data masking rules in LLM setup.
  • C. Enable data masking for sandbox refreshes.

Answer: A

Explanation:
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist References:For more information, refer to: https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer_data_masking.htm


NEW QUESTION # 111
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