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

You are tasked with building a Retrieval-Augmented Generation (RAG) system to assist users in retrieving relevant documents from a vast knowledge base. The first step in this process is to generate vector embeddings for the documents using a pre-trained model. After generating embeddings, you notice that the model is sometimes failing to retrieve semantically similar documents.
Which of the following is the most appropriate approach to ensure that semantically similar documents are retrieved effectively?

• A. Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.
• B. Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
• C. Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
• D. Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.

You are tasked with designing prompts for an IBM Watsonx Generative AI model to minimize hallucinations in responses. One of the ways to reduce hallucinations is by improving the quality of the prompt to guide the model more effectively.
Which of the following prompt engineering strategies would be most effective in reducing the likelihood of hallucinations?

• A. Include explicit instructions and specific constraints within the prompt to limit the scope of the model's generation.
• B. Set the minimum token length high to ensure the model has enough time to fully develop its response.
• C. Use highly abstract and open-ended prompts to allow the model more freedom in generating responses.
• D. Increase the temperature parameter to introduce more diversity and creativity into the model's output.

In the context of zero-shot prompting, you are developing a Watsonx AI model to generate a summary of financial reports. You input the following prompt: "Summarize the financial report in one sentence." Based on your understanding of zero-shot prompting, what outcome should you expect from the model?

• A. The model may generate a reasonable summary based solely on the prompt, even if it hasn't been explicitly trained on financial report summaries.
• B. he model will refuse to generate a response due to lack of examples provided in the prompt.
• C. The model will ask for additional data before generating the summary.
• D. The model will output a random sentence because zero-shot prompting is unreliable without examples.

You are tasked with generating synthetic data for fine-tuning a large language model (LLM) using the IBM Watsonx User Interface. You want to generate relevant training samples to improve the model's accuracy in a text classification task.
Which action should you prioritize to generate high-quality synthetic data using the interface?

• A. Avoid using domain-specific prompts to keep the synthetic data unbiased and more generalizable.
• B. Customize prompt templates to closely mimic the structure and format of the original training data.
• C. Select only a few generic prompts to generate the largest volume of data possible, ensuring variety.
• D. Choose a diverse set of prompts that span different domains unrelated to the original dataset.