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| Section | Weight | Objectives |
|---|---|---|
| Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Einstein Trust Layer & Security | - Data Masking
| |
| Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
| Reasoning Engine & AI Actions | - Workflow mastery
|
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NEW QUESTION # 152
Before activating a custom Agent action, an Agentforce Specialist would like to understand multiple real-world user utterances to ensure the action is being selected appropriately. Which tool should the Specialist recommend?
Answer: C
NEW QUESTION # 153
Universal Containers implements Custom Agent Actions to enhance its customer service operations. The development team needs to understand the core components of a Custom Agent Action to ensure proper configuration and functionality. What should the development team review in the Custom Agent Action configuration to identify one of the core components of a Custom Agent Action?
Answer: B
Explanation:
UC's development team needs to identify a core component of a Custom Agent Action in Agent Builder.
NEW QUESTION # 154
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS). What should the team use to access externally-hosted models in the Salesforce Platform?
Answer: C
Explanation:
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
NEW QUESTION # 155
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: A
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.
NEW QUESTION # 156
Universal Containers (UC) configured a new PDF file ingestion in Data Cloud with all the required fields, and also created the mapping and the search Index. UC Is now setting up the retriever and notices a required fleld is missing. How should UC resolve this?
Answer: A
Explanation:
Why is "Update the search index to include the desired field" the correct answer?
When configuring a retriever in Data Cloud for PDF file ingestion, all necessary fields must be included in the search index. If a required field is missing, the correct action is to update the search index to ensure it is available for retrieval.
Key Considerations for Fixing Missing Fields in Data Cloud Retrievers:
Search Index Controls Which Fields Are Searchable
The search index defines which fields are indexed and accessible to the retriever.
If a field is missing, it must be added to the index before it can be queried.
Ensures Complete and Accurate Data Retrieval
Without indexing, the retriever cannot reference the missing field in AI responses.
Updating the index makes the field available for AI-powered retrieval.
Supports AI-Grounded Responses
Agentforce relies on Retriever-Augmented Generation (RAG) to ground AI responses in searchable Data Cloud content.
Ensuring all relevant fields are indexed improves AI-generated answer accuracy.
NEW QUESTION # 157
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