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1. You are tasked with optimizing a large language model (LLM) for deployment in a resource-constrained environment where memory usage and computational cost need to be minimized without significantly compromising model accuracy.
Which quantization technique would be the most appropriate to achieve this balance?

A) Mixed-precision floating-point quantization
B) Full integer quantization with 8-bit precision
C) Post-training weight clustering
D) Post-training dynamic quantization

2. Which of the following stopping criteria can help in generating coherent and well-structured text without cutting off mid-sentence or continuing unnecessarily?

A) Monitoring the likelihood of the next token and stopping when the likelihood drops below a threshold
B) Stopping when the model generates special end-of-sequence tokens, such as <EOS>
C) Stopping the model after a predetermined number of tokens, regardless of context
D) Stopping the model only when it reaches the end of a predefined phrase from the input prompt

3. In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A) Retraining the model based on emerging trends in data
B) Deployment to production with regular monitoring and logging
C) Iterative prompt tuning based on A/B test results and feedback loops
D) Initial testing on synthetic datasets and model validation

4. You are tasked with improving the performance of a Retrieval-Augmented Generation (RAG) system in IBM watsonx. Part of this improvement involves selecting the right embedding model for document retrieval.
Which of the following is the best description of the differences between various embedding models, and how would you choose the most suitable model for your task?

A) TF-IDF is an advanced embedding model that captures both the frequency and semantic meaning of words, making it more effective than deep learning-based models like BERT for retrieval in RAG systems.
B) BERT embeddings are context-independent, which makes them less useful for a RAG system than Word2Vec or GloVe, which focus on learning semantic relationships between words.
C) Word2Vec embeddings capture only the syntactic relationships between words, while BERT embeddings focus on both syntax and semantic context, making BERT more suitable for complex retrieval tasks in a RAG system.
D) Word2Vec, GloVe, and BERT are all embedding models, but BERT embeddings capture richer context by considering the entire sentence rather than just the local context, making it more effective for generating semantically relevant embeddings.

5. You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A) Gradually increase the number of epochs until the loss on the training set reaches zero
B) Set a high number of epochs and use early stopping to determine the optimal point
C) Use a single epoch to avoid overfitting
D) Set the number of epochs equal to the size of the dataset

問題與答案：