You are tasked with developing a customer support system for an e-commerce platform using the Retrieval-Augmented Generation (RAG) pattern. The system needs to retrieve relevant information from a large database of product specifications, user manuals, and FAQs. You decide to use LangChain for constructing the pipeline and SingleStore as the backend for storing and querying the document embeddings. The objective is to efficiently retrieve semantically similar documents and use them as input for a generative model that crafts human-like responses.
Which of the following steps best describes the correct implementation of the RAG pattern using LangChain and SingleStore for this customer support system?

• A. Use LangChain to fine-tune the generative model, then store the embeddings in SingleStore, and use SQL queries to retrieve documents based on exact keyword matches.
• B. Store the pre-trained generative model's parameters in SingleStore and use LangChain to retrieve embeddings from the database for training the model.
• C. Use LangChain to generate embeddings and directly store the generated responses in SingleStore without any retrieval mechanism.
• D. Use LangChain to create an LLM chain that integrates with SingleStore for embedding retrieval, where the retrieved documents are used as context for generating responses.

You are deploying a generative AI model to summarize legal documents. During testing, you observe that the model sometimes produces inaccurate or fabricated information about legal clauses that do not exist in the original document.
Which of the following strategies would be the most effective in reducing hallucinations in the model's output?

• A. Increase the model's temperature to allow for more diverse outputs.
• B. Use beam search with a high beam width to generate the most likely sequence.
• C. Implement a post-processing step that filters for factual accuracy using a knowledge base.
• D. Reduce the maximum token limit and set a higher repetition penalty.

You are tasked with fine-tuning a large language model (LLM) on a specific industry dataset using the IBM watsonx user interface. Due to the lack of labeled data, your team decides to generate synthetic data to supplement the training set. The objective is to ensure the fine-tuned model can generalize effectively to real-world scenarios in this industry. You need to configure synthetic data generation and perform the fine-tuning.
Which of the following actions should you take to ensure the synthetic data is suitable for fine-tuning the model and does not lead to overfitting or model bias? (Select two)

• A. Ensure that the synthetic data covers edge cases as well as common industry scenarios.
• B. Rely solely on synthetic data for fine-tuning, as it is fully representative of the industry domain.
• C. Configure the synthetic data to exclude rare or uncommon events, as these are not representative of the overall dataset.
• D. Use the IBM watsonx Data Refinery tool to inspect and balance the synthetic data before fine-tuning.
• E. Generate only a small amount of synthetic data to minimize computational costs and training time.

A generative AI model is given the following prompt: "Translate the following sentence into French: 'The sun is shining brightly today.'" No additional context or examples are provided.
This is an example of which type of prompting and why is it likely to succeed?

• A. Zero-shot prompting, but it will likely fail since translation tasks always need examples for accuracy.
• B. Zero-shot prompting, because the model is expected to perform the task without any example.
• C. Few-shot prompting, because the task requires translation examples to guide the model.
• D. Zero-shot prompting, but it will only succeed if the prompt includes a few additional translation examples.