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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with creating a prompt template for IBM Watsonx that will help generate product reviews for a new line of smartphones. The prompt needs to be adaptable across various product features and sentiment (positive, neutral, or negative) while maintaining a consistent structure.
Which of the following prompt templates would be the most effective for generating well-structured, detailed reviews?

A) "Write a review about the smartphone."
B) "Write a [sentiment] review of the [product name], focusing on the [specific feature]. Include reasons for the sentiment and detailed examples."
C) "Tell a story about the user's experience with the smartphone."
D) ":Generate a review mentioning the best features of the smartphone."

2. You are building a RAG system that integrates LlamaIndex for efficient document retrieval from a knowledge base containing millions of documents.
Which of the following is the most critical consideration for ensuring the system performs well at scale?

A) Configuring LlamaIndex to perform real-time indexing of incoming data streams to ensure the most up-to-date documents are always retrieved.
B) Adjusting LlamaIndex to prioritize documents that contain certain predefined keywords, optimizing retrieval for specific business terms.
C) Setting LlamaIndex to index only a small subset of the documents to minimize the time it takes to retrieve any relevant document.
D) Ensuring LlamaIndex generates unique embedding vectors for each document and storing them in an optimized vector database for quick lookups.

3. You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?

A) Defining task-specific instructions and fine-tuning them through prompt design.
B) Running evaluation metrics on the baseline model to measure initial performance.
C) Pre-processing and augmenting the training data to improve the model's generalization capabilities.
D) Deploying the model in a real-time environment for user feedback collection.

4. In the context of model quantization for generative AI, which of the following statements correctly describes the impact of quantization techniques on model performance and resource efficiency? (Select two)

A) Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.
B) Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.
C) Quantization reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
D) Quantizing a model to 8-bit precision always results in a significant loss in performance, especially when working with language models or large generative AI architectures.
E) Post-training quantization is more resource-efficient than quantization-aware training, as it applies quantization after the model has been fully trained, eliminating the need for additional fine-tuning.

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

Solutions: