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[Hardware] Latest Salesforce AI-201 Exam Fee, New AI-201 Exam Experience

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【Hardware】 Latest Salesforce AI-201 Exam Fee, New AI-201 Exam Experience

Posted at yesterday 23:53      View：3 | Replies：0        Print      Only Author   [Copy Link] 1#
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Salesforce Certified Agentforce Specialist Sample Questions (Q39-Q44):NEW QUESTION # 39
A company wants to retrieve patient history details to augment the AI agent response and plans to use the Data Cloud search index feature. What is best practice when considering retrieval- augmented generation (RAG) for information that may contain personally identifiable information (PII)?
  • A. Encrypt embeddings, but still index PII records
  • B. Depend on the agent's prompt to avoid exposing PII
  • C. Mask sensitive fields and index only non-PII data
Answer: C

NEW QUESTION # 40
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
  • A. Ensure queries used for grounding employ offset so the token limits of models are not exceeded.
  • B. Review the token limit in the Einstein Trust Layer.
  • C. Review the model limitation in Prompt Builder versus the grounding data size.
Answer: C
Explanation:
The most critical technical consideration when pairing a prompt template's grounding data with a chosen Large Language Model (LLM) is the relationship between the two. The correct action is to review the model limitation in Prompt Builder versus the grounding data size (C).
Every LLM has a fixed context window limit, typically expressed in tokens (the model's units for processing text). This token limit defines the maximum amount of input data (the prompt template text + all the dynamic grounding data) and output data the model can handle in a single request.
The grounding data, which is pulled dynamically from Salesforce records (e.g., related lists, long text fields, Flow outputs), varies significantly in size from one record to the next. If the combined size of the prompt and the dynamic data for a specific record exceeds the LLM's token limit, the generative AI request will fail with a "token limit exceeded" error. The Agentforce Specialist must proactively design the template to limit the amount of data retrieved (e.g., using Flow to summarize related lists or querying only essential fields) to ensure it stays within the chosen model's capacity.

NEW QUESTION # 41
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?
  • A. Convert the JSON to an XML merge field.
  • B. Use the 'Add Prompt Instructions' flow element.
  • C. Use External Service Record merge fields.
Answer: C
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.

NEW QUESTION # 42
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities. What is one criterion that Agentforce for Sales uses to match similar opportunities?
  • A. Matched opportunities were created in the last 12 months.
  • B. Matched opportunities are limited to the same account.
  • C. Matched opportunities have a status of Closed Won from the last 12 months.
Answer: C
Explanation:
UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure.

NEW QUESTION # 43
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?
  • A. Field Generation, and that Dynamic Fields is enabled
  • B. Flex, and that Dynamic Fields is enabled
  • C. Field Generation, and that Dynamic Forms is enabled
Answer: A
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 to populate a field with generated output, which directly aligns with the Field Generation prompt 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 the Einstein 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 whether Dynamic Fields is 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.

NEW QUESTION # 44
......
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