TRAINING AGENTFORCE-SPECIALIST PDF - AGENTFORCE-SPECIALIST RELIABLE DUMPS SHEET

Training Agentforce-Specialist Pdf - Agentforce-Specialist Reliable Dumps Sheet

Training Agentforce-Specialist Pdf - Agentforce-Specialist Reliable Dumps Sheet

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Tags: Training Agentforce-Specialist Pdf, Agentforce-Specialist Reliable Dumps Sheet, Agentforce-Specialist Practice Tests, Agentforce-Specialist Discount Code, Agentforce-Specialist Question Explanations

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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.

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

NEW QUESTION # 106
Leadership needs to populate a dynamic form field with a summary or description created by a large language model (LLM) to facilitate more productive conversations with customers. Leadership also wants to keep a human in the loop to be considered in their AI strategy. Which prompt template type should the Agentforce Specialist recommend?

  • A. Record Summary
  • B. Sales Email
  • C. Field Generation

Answer: C

Explanation:
Why is "Field Generation" the correct answer?
In Agentforce, the Field Generation prompt template type is designed to populate dynamic form fields with AI-generated content, such as summaries or descriptions created by a large language model (LLM).
Key Considerations for Using Field Generation in Dynamic Forms:
* AI-Powered Summarization in Form Fields
* Field Generation templates allow real-time AI-generated summaries based on customer data.
* The summary is dynamically populated in the form field for the sales or service representative to review.
* Human-in-the-Loop AI Strategy
* Since leadership wants a human to be involved, Field Generation ensures the AI-generated content is editable before submission.
* This keeps a human-in-the-loop, allowing manual review before finalizing responses.
* Works with Salesforce Dynamic Forms
* Field Generation templates integrate seamlessly with Salesforce Dynamic Forms, ensuring AI- powered insights are embedded within form layouts.
Why Not the Other Options?
# B. Sales Email
* Incorrect because Sales Email templates are designed for AI-generated email content, not for populating form fields.
# C. Record Summary
* Incorrect because Record Summary templates generate high-level summaries of entire records, but do not populate individual form fields dynamically.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that Field Generation templates are used for AI- powered dynamic form population.


NEW QUESTION # 107
An account manager is preparing for an upcoming customer call and wishes to get a snapshot of key data points from accounts, contacts, leads, and opportunities in Salesforce.
Which feature provides this?

  • A. Sales Insight Summary
  • B. Work Summaries
  • C. Sales Summaries

Answer: A

Explanation:
Sales Insight Summary aggregates key data points from multiple Salesforce objects (accounts, contacts, leads, opportunities) into a consolidated view, enabling account managers to quickly access relevant information for customer calls.
* Option A (Sales Summaries): Typically refers to Einstein-generated summaries of specific interactions (e.g., emails, calls), not multi-object snapshots.
* Option C (Work Summaries): Focuses on summarizing customer service interactions (e.g., chat transcripts), not sales data.
* Option B (Sales Insight Summary): Directly provides a holistic snapshot of sales-related objects, aligning with the scenario.
References:
* Salesforce Help: Sales Insight Overview
* Describes Sales Insight Summary as "a unified view of account, contact, and opportunity data for sales readiness."


NEW QUESTION # 108
Universal Containers (UC) wants to enable its sales team to get insights into product and competitor names mentioned during calls. How should UC meet this requirement?

  • A. Enable Einstein Conversation Insights, assign permission sets, define recording managers, and customize insights with up to 50 competitor names.
  • B. Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products.
  • C. Enable Einstein Conversation Insights, connect a recording provider, assign permission sets, and customize insights with up to 25 products.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC wants insights into product and competitor mentions during sales calls, leveraging Einstein Conversation Insights. Let's evaluate the options.
* Option A: Enable Einstein Conversation Insights, connect a recording provider, assign permission sets, and customize insights with up to 25 products.Einstein Conversation Insights analyzes call recordings to identify keywords like productand competitor names. Setup requires enabling the feature, connecting an external recording provider (e.g., Zoom, Gong), assigning permission sets (e.g., Einstein Conversation Insights User), and customizing insights by defining up to
25 products or competitors to track. Salesforce documentation confirms the 25-item limit for custom keywords, making this the correct, precise answer aligning with UC's needs.
* Option B: Enable Einstein Conversation Insights, assign permission sets, define recording managers, and customize insights with up to 50 competitor names.There's no "recording managers" role in Einstein Conversation Insights setup-integration is with a provider, not a manager designation.
The limit is 25 keywords (not 50), and the option omits the critical step of connecting a provider, making it incorrect.
* Option C: Enable Einstein Conversation Insights, enable sales recording, assign permission sets, and customize insights with up to 50 products."Enable sales recording" is vague-Conversation Insights relies on external providers, not a native Salesforce recording feature. The keyword limit is 25, not 50, making this incorrect despite being closer than B.
Why Option A is Correct:Option A accurately reflects the setup process and limits for Einstein Conversation Insights, meeting UC's requirement per Salesforce documentation.
References:
* Salesforce Help: Set Up Einstein Conversation Insights- Details provider connection and 25-keyword limit.
* Trailhead: Einstein Conversation Insights Basics- Covers permissions and customization.
* Salesforce Agentforce Documentation: Sales Features- Confirms integration steps.


NEW QUESTION # 109
After configuring and saving a Salesforce Agentforce Data Library (regardless of the data source), which components are automatically created and available in Data Cloud?

  • A. A data connector, an analytics dashboard, and a workflow rule
  • B. A data pipeline, an indexing engine, and a query processor
  • C. A data stream, a search index, and a retriever

Answer: C

Explanation:
Why is "A data stream, a search index, and a retriever" the correct answer?
When a Salesforce Agentforce Data Library is configured and saved, it automatically creates three essential components in Data Cloud to facilitate AI-driven search and retrieval.
Key Components Created in Data Cloud:
* Data Stream
* This acts as the pipeline that brings data into Data Cloud.
* It enables real-time data ingestion from sources such as Salesforce records, PDFs, or external databases.
* Search Index
* After ingestion, data is indexed for efficient search and retrieval.
* This allows AI models to perform structured queries and retrieve relevant data faster.
* Retriever
* The retriever is an AI-powered search mechanism that uses the search index to fetch the most relevant data.
* It ensures that AI-generated responses are grounded in structured, reliable data.
Why Not the Other Options?
# A. A data pipeline, an indexing engine, and a query processor
* Incorrect because Data Cloud does not use a query processor in the same way as traditional databases.
* Instead, retrievers handle AI-powered data searches.
# B. A data connector, an analytics dashboard, and a workflow rule
* Incorrect because these components are not automatically created when setting up a Data Library.
* Analytics dashboards and workflow rules are separate tools used for reporting and automation.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that a Data Stream, Search Index, and Retriever are created automatically in Data Cloud when configuring a Data Library.


NEW QUESTION # 110
How does the AI Retriever function within Data Cloud?

  • A. It performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
  • B. It monitors and aggregates data quality metrics across various data pipelines to ensure only high- integrity data is used for strategic decision-making.
  • C. It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:The AI Retriever is a key component in Salesforce Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let's evaluate each option based on its documented functionality.
* Option A: It performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.
The AI Retriever in Data Cloud uses vector-based search technology to query an indexed repository (e.
g., documents, records, or ingested data) and retrieve the most relevant results based on context. It employs embeddings to match user queries or prompts with stored data, ensuring AI responses (e.g., in Agentforce prompt templates) are grounded in accurate, verifiable information from Data Cloud. This enhances trustworthiness by linking outputs to source data, making it the primary function of the AI Retriever. This aligns with Salesforce documentation and is the correct answer.
* Option B: It monitors and aggregates data quality metrics across various data pipelines to ensure only high-integrity data is used for strategic decision-making.Data quality monitoring is handled by other Data Cloud features, such as Data Quality Analysis or ingestion validation tools, not the AI Retriever. The Retriever's role is retrieval, not quality assessment or pipeline management. This option is incorrect as it misattributes functionality unrelated to the AI Retriever.
* Option C: It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.Data extraction and standardization are part of Data Cloud's ingestion and harmonization processes (e.g., via Data Streams or Data Lake), not the AI Retriever's function. The Retriever works with already-indexed data to fetch results, not to process or reformat raw data. This option is incorrect.
Why Option A is Correct:The AI Retriever's core purpose is to perform contextual searches over indexed data, enabling AI grounding with reliable information. This is critical for Agentforce agents to provide accurate responses, as outlined in Data Cloud and Agentforce documentation.
References:
* Salesforce Data Cloud Documentation: AI Retriever- Describes its role in contextual searches for grounding.
* Trailhead: Data Cloud for Agentforce- Explains how the AI Retriever fetches relevant data for AI responses.
* Salesforce Help: Grounding with Data Cloud- Confirms the Retriever's search functionality over indexed repositories.


NEW QUESTION # 111
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