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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with designing a prompt for a sentiment analysis model based on a large language model (LLM). The goal is to generate a coherent response from the model that aligns with a particular sentiment (positive, negative, or neutral) for customer reviews of a product.
Which of the following prompt designs are best suited to generate a positive review response? (Select two)

A) "Write a review about the product that highlights both its pros and cons."
B) "Describe the product as if you were a very satisfied customer, and you were recommending it to a friend."
C) "Write a neutral review, neither praising nor criticizing the product."
D) "Analyze the product based on the customer feedback and write a review that covers all sentiments."
E) "Generate a positive review about the product, focusing on the key strengths and avoiding any negative aspects."

2. 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) Choose a model with a smaller embedding dimension to reduce the memory footprint of embeddings.
C) Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
D) Fine-tune the model on a task-specific dataset to improve the quality of the embeddings for your domain.

3. You are fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A) Increase the model's batch size during training
B) Increase the number of layers in the model
C) Use domain-specific tokenization during fine-tuning
D) Add more general-purpose data to the fine-tuning dataset

4. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
B) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.
C) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.
D) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.

5. You are tasked with creating a prompt template for IBM Watsonx to generate customer support responses based on user queries. The response needs to be polite, concise, and address the issue directly.
Which of the following is the most appropriate structure for a reusable prompt template to ensure consistency across multiple queries?

A) "Generate a professional response to the customer's query, avoiding repetition and unnecessary details, while focusing on addressing the issue succinctly."
B) "Write a short and casual response to the customer, focusing on being friendly and engaging, regardless of the content of the query."
C) "Please write a polite and professional response to the customer's query, including any relevant context or background information and focusing on the core issue."
D) "Generate a detailed and formal response to the customer, focusing on providing as much information as possible, even if it's unrelated to the query."

Solutions: