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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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 4	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 5	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.

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Salesforce Certified Agentforce Specialist Sample Questions (Q122-Q127):

NEW QUESTION # 122
An Agentforce Agent has been developed with multiple topics and Agent Actions that use flows and Apex.
Which options are available for deploying these to production?

A. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
B. Use only change sets because the Salesforce CLI does not currently support the deployment of agent- related metadata.
C. Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
/Metadata API.

Answer: C

Explanation:
Why is "Deploy flows, Apex, and all agent-related items using either change sets or the Salesforce CLI
/Metadata API" the correct answer?
When deploying an Agentforce Agent with multiple topics and Agent Actions that use flows and Apex, a complete deployment solution is required. Change sets and the Salesforce CLI/Metadata API support the deployment of flows, Apex code, and agent-related metadata.
Key Considerations for Agentforce Deployments:
* Supports Deployment of All Required Components
* Agentforce Agents include flows, Apex classes, topics, and agent actions.
* Change sets and Salesforce CLI/Metadata API allow deployment of all these components together, ensuring a smooth transition to production.
* Agentforce Metadata Can Be Deployed Using Standard Tools
* Change Sets: Allows admins to move configurations, custom objects, and metadata between Salesforce environments.
* Salesforce CLI/Metadata API: Enables scripted deployments, automating the transfer of Agentforce configurations.
* Ensures a Complete Migration Without Manual Configuration
* Deploying all components together reduces the risk of misconfiguration.
* Automating deployments using the Metadata API ensures consistency across environments.
Why Not the Other Options?
# A. Deploy the flows and Apex using normal deployment tools and manually create the agent-related items in production.
* Incorrect because manually creating agent-related items in production introduces risk and inconsistency.
* This approach is error-prone and time-consuming, especially for large Agentforce deployments.
# B. Use only change sets because the Salesforce CLI does not currently support the deployment of agent-related metadata.
* Incorrect because Salesforce CLI and Metadata API fully support Agentforce deployments.
* Change sets are useful but limited in large-scale, automated deployments.
Agentforce Specialist References
* Salesforce AI Specialist Material confirms that Agentforce metadata (flows, actions, and topics) can be deployed using Change Sets or the Metadata API.

NEW QUESTION # 123
Based on the user utterance, 'Show me all the customers in New York', which standard Agent action will the planner service use?

A. Query Records
B. Fetch Records
C. Select Records

Answer: A

Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them. When a user issues a command like:"Show me all the customers in New York", the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
* Retrieves Data Based on Specific Field Values
* This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
* Uses standard or custom object fields (e.g., BillingState = 'New York').
* Works with Large Language Models (LLMs) and Copilot Actions
* When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
* Optimized for Structured Data Retrieval
* Ensures AI retrieves relevant CRM records quickly and accurately.
Why Not the Other Options?
# B. Fetch Records
* This is not a standard term in Einstein Copilot or Agentforce.
* No defined Agentforce action exists under this name.
# C. Select Records
* Select Records is used to pick records from an already presented list, not to retrieve them initially.
* If the user had already retrieved records and wanted to refine their selection, Select Records might be appropriate.
* However, since the user's request is to retrieve records, Query Records is the correct action.
Agentforce Specialist References
This information is confirmed from the Salesforce AI Specialist Material and Questions Document, where the Query Records action is explicitly defined as the appropriate standard action for retrieving filtered CRM records.

NEW QUESTION # 124
How does the AI Retriever function within Data Cloud?

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

Answer: C

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 # 125
Universal Containers built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors. What is the cause of the random nature of this error?

A. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
B. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
C. The number of tokens that can be processed by the LLM varies with total user demand.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). Therandom natureof these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.
* Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they're unrelated to token limits). This option is a distractor and not a verified solution.
* Option C: The LLM's token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.
References:
* Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help:https://help.salesforce.com
/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
* Trailhead: "Build Prompt Templates for Agentforce"(https://trailhead.salesforce.com/content/learn
/modules/build-prompt-templates-for-agentforce)

NEW QUESTION # 126
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. Create a flow to retrieve the opportunity information.
B. Select the latest Opportunities related list as a merge field.
C. Select the Account Opportunity object as a resource when creating the prompt template.

Answer: A

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 # 127
......

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