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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Exam Format: | Multiple Select, Scenario-Based, Multiple Choice |
| Certificate Validity Period: | 1 year (requires annual maintenance) |
| Real Exam Qty: | 60-65 |
| Exam Duration: | 105 minutes |
| Passing Score: | 73% |
| Available Languages: | English |
| Exam Price: | $200 USD |
| Related Certifications: | Salesforce Certified AI Specialist |
| Recommended Training: | Trailhead Agentforce Specialist Certification Prep |
| Exam Registration: | Salesforce Certification Registration Pearson VUE Scheduling |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: 6 months experience with Agentforce or Salesforce AI features |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/certification-detail-print/?certificationId=286 |
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NEW QUESTION # 179
An Agentforce Specialist is trying to troubleshoot a reported issue from an end user, and they do not see the session in the Processed Sessions tab of Agentforce Observability.
What should the specialist take into consideration?
Answer: A
Explanation:
The correct answer is B. If a session is not visible under Processed Sessions, the specialist should first consider that it may not have completed processing yet. Agentforce Observability and Session Tracing are designed to collect, store, and expose detailed session data for troubleshooting, but session data can move through processing states before it appears in the processed view. Option A is too broad because the absence of one session does not automatically prove the agent is unregistered. Option C is also too rigid because the correct operational check is the unprocessed queue, not simply waiting for a daily batch cycle. For troubleshooting, the specialist should inspect the Unprocessed Sessions tab before assuming missing observability setup or lost trace data.
NEW QUESTION # 180
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?
Answer: A
Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.
Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm
NEW QUESTION # 181
Choose 1 option.
Universal Containers (UC) has registered an external service and created a template-triggered prompt flow that invokes the external service to fetch data from a REST API. UC now needs to make the response data from the external service usable inside a prompt template as a merge field when the template runs.
How should UC meet this requirement?
Answer: A
Explanation:
As outlined in the AgentForce External Services and Prompt Flow Integration Guide, when data is retrieved from a registered external service via REST API, the response payload is stored as External Service Records.
These records can then be referenced dynamically within prompt templates through External Service Record merge fields.
This approach allows the large language model (LLM) to use the fetched data as contextual grounding during prompt execution, ensuring that generated responses are accurate and consistent with the latest API results.
Option B is incorrect because AgentForce does not use XML merge fields for API responses; JSON data is automatically mapped to object structures. Option C is also incorrect - the "Add Prompt Instructions" element modifies prompt context or tone but does not pass external data for merge use.
Therefore, the correct method is Option A - Use External Service Record merge fields, ensuring the external service data is directly available for prompt templates.
Reference: AgentForce Developer Guide - "Integrating External Services and Using Merge Fields in Prompt Flows."
NEW QUESTION # 182
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses.
UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?
Answer: B
Explanation:
In Salesforce Agentforce, a custom retriever is used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses. UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is to define filters (Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the
'Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.
Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.
Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.
Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.
Option A is the most effective step to ensure relevance in retrieved data.
Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)
NEW QUESTION # 183
Universal Containers (UC) is creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements. Which prompt template type should UC use and which consideration should UC review?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Salesforce Agentforce provides various prompt template types to support AI-driven tasks, such as generating text or populating fields. In this case, UC needs a custom prompt template topopulate a field with generated output, which directly aligns with theField Generationprompt template type. This type is designed to use generative AI to create field values (e.g., summaries, descriptions) based on input data or prompts, making it the ideal choice for UC's requirement. Additionally, UC has enabled theEinstein Trust Layer, a governance framework that ensures AI outputs are safe, explainable, and auditable, capturing AI Audit data for monitoring adoption and identifying improvement areas.
The consideration UC should review is whetherDynamic Fieldsis enabled. Dynamic Fields allow the prompt template to incorporate variable data from Salesforce records (e.g., case details, customer info) into the prompt, ensuring the generated output is contextually relevant to each record. This is critical for field population tasks, as static prompts wouldn't adapt to record-specific needs. The Einstein Trust Layer further benefits from this, as it can track how dynamic inputs influence outputs for audit purposes.
* Option A: Correct. "Field Generation" matches the use case, and "Dynamic Fields" is a key consideration to ensure flexibility and auditability with the Trust Layer.
* Option B: "Field Generation" is correct, but "Dynamic Forms" is unrelated. Dynamic Forms is a UI feature for customizing page layouts, not a prompt template setting, making this option incorrect.
* Option C: "Flex" templates are more general-purpose and not specifically tailored for field population tasks. While Dynamic Fields could apply, Field Generation is the better fit for UC's stated goal.
Option A is the best choice, as it pairs the appropriate template type (Field Generation) with a relevant consideration (Dynamic Fields) for UC's scenario with the Einstein Trust Layer.
:
Salesforce Agentforce Documentation: "Prompt Template Types" (Salesforce Help:https://help.salesforce.com
/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Monitor AI with Trust Layer" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)
Trailhead: "Build Prompt Templates for Agentforce" (https://trailhead.salesforce.com/content/learn/modules
/build-prompt-templates-for-agentforce)
NEW QUESTION # 184
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