What does the Prompt Management feature of the SAP AI launchpad allow users to do?
Correct Answer: A,D
Question 2
What is the primary function of the embedding model in a RAG system?
Correct Answer: D
Question 3
Why would a user include formatting instructions within a prompt?
Correct Answer: D
Question 4
Which of the following steps must be performed to deploy LLMs in the generative Al hub?
Correct Answer: B
Deploying Large Language Models (LLMs) in SAP's Generative AI Hub involves a structured process: 1. Provision SAP AI Core: * Setup:Ensure that SAP AI Core is provisioned in your SAP Business Technology Platform (BTP) account to manage AI workloads. 2. Check for Foundation Model Scenario: * Validation:Verify the availability of the foundation model scenario within SAP AI Core to confirm that the necessary resources and configurations are in place for deploying LLMs. 3. Create a Configuration: * Configuration Setup:Define the parameters and settings required for the LLM deployment, including model specifications and resource allocations. 4. Create a Deployment: * Deployment Execution:Initiate the deployment process within SAP AI Core, making the LLM available for integration and use within your applications.
Question 5
Which of the following is a benefit of using Retrieval Augmented Generation?
Correct Answer: A
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling them to access and utilize information beyond their initial training data. 1. Understanding Retrieval-Augmented Generation (RAG): * Definition:RAG combines the generative capabilities of LLMs with retrieval mechanisms that access external knowledge bases or documents. This integration allows the model to incorporate up-to-date and domain-specific information into its responses. * Mechanism:When presented with a query, the RAG system retrieves pertinent information from external sources and uses this data to inform and generate a more accurate and contextually appropriate response. 2. Benefits of RAG: * Access to External Information:RAG allows LLMs to access and utilize information beyond their initial training data, enabling them to provide more accurate and relevant responses. * Up-to-Date Information:Since RAG systems can query current data sources, they are capable of providing the most recent information available, which is crucial in dynamic fields. * Improved Accuracy and Relevance:By leveraging external data, RAG enhances theaccuracy and relevance of the generated content, making it particularly useful for tasks requiring detailed or domain- specific information.
