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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. When using IBM Watsonx Tuning Studio, what is the recommended approach to determining the number of training data examples required for effective model fine-tuning?

A) Use at least 10,000 examples for each unique task to ensure the model retains its general knowledge and effectively adapts to the new task.
B) Use at least 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.
C) Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
D) Use a minimum of 1,000 to 5,000 examples for each task, but focus on the quality and relevance of examples rather than quantity.

2. A large language model you are fine-tuning occasionally generates completely fabricated references and citations when responding to user queries. This behavior exemplifies a specific model risk.
Which of the following techniques would most effectively reduce this risk in a production environment?

A) Increasing the model's response diversity by adjusting top-p sampling
B) Switching to greedy decoding for more deterministic responses
C) Using human-in-the-loop (HITL) methods for real-time validation
D) Deploying rule-based post-processing filters to validate the output

3. You are selecting a model to fine-tune using Tuning Studio for a financial application that requires high accuracy and domain-specific language understanding.
Which type of model should you select to maximize fine-tuning efficiency and performance?

A) A model pre-trained on financial and business data but with limited language capabilities.
B) A small pre-trained model specifically designed for open-domain tasks.
C) A pre-trained model that has been optimized for creative text generation.
D) A large pre-trained language model that has been fine-tuned on generic business communication.

4. You are working with a Watsonx Generative AI model to create marketing content that balances creativity with efficiency. The goal is to generate engaging content within a predefined time limit without compromising on quality.
Given this context, which two optimization strategies will most effectively help you achieve both speed and content quality? (Select two)

A) Apply top-k sampling with k = 50 to ensure diverse and creative outputs.
B) Increase the model's learning rate to accelerate content generation.
C) Use beam search with a beam width of 1 to minimize computation time.
D) Implement early stopping criteria based on token repetition to avoid lengthy generation.
E) Reduce the batch size to decrease training time and speed up generation.

5. 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) Use Greedy Decoding during the embedding generation to avoid irrelevant tokens in the vectors.
B) Convert all documents into embeddings using cosine similarity directly instead of using a vector search algorithm.
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.

Solutions: