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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. After prompt-tuning a language model, you notice that certain outputs are semantically correct but syntactically flawed.
Which of the following actions is most appropriate to resolve this issue and optimize the tuned model's performance?

A) Use a higher temperature during the generation process
B) Fine-tune the prompt template to emphasize grammar
C) Lower the learning rate during the tuning phase
D) Increase the model's training dataset size

2. You are optimizing a large language model (LLM) for deployment on edge devices with limited computational resources.
To reduce the model size and improve efficiency without significantly compromising performance, which of the following quantization techniques is most appropriate for this scenario?

A) Binary quantization (1-bit)
B) Post-training 16-bit floating point quantization
C) Post-training 8-bit integer quantization
D) 32-bit floating point quantization with fine-tuning

3. In the context of sampling decoding for IBM Watsonx Generative AI, which of the following statements best describes how top-k sampling works?

A) Top-k sampling automatically filters out low-probability tokens that were not part of the model's training set.
B) Top-k sampling selects the next token only from the top k most probable tokens based on their probabilities.
C) Top-k sampling selects the token with the highest probability, ignoring all other token options.
D) Top-k sampling ensures that the next token is chosen only if it matches one of the predefined input variables.

4. Which of the following statements best describes the primary advantage of applying quantization to a large language model (LLM) during inference?

A) Quantization allows the model to learn more efficiently during training by focusing on fewer parameters.
B) Quantization primarily reduces the size of the training dataset required for an LLM.
C) Quantization lowers computational requirements, enabling faster inference with minimal impact on model accuracy.
D) Quantization automatically improves the accuracy of an LLM by converting all weights to higher precision.

5. You are designing a question-answering system that can provide responses based on a vast corpus of legal documents. Your solution leverages the Retrieval-Augmented Generation (RAG) pattern using an IBM watsonx-based architecture. The goal is to ensure that the system retrieves highly relevant documents and uses the retrieved content to generate accurate responses. The team is considering various libraries to integrate dense retrieval and generation models into this architecture.
Which of the following libraries or frameworks should you integrate to effectively implement the RAG pattern in this scenario, considering the need for both document retrieval and natural language generation?

A) Hugging Face Transformers for language generation and FAISS for embedding-based document retrieval.
B) TensorFlow for dense retrieval and document embedding generation.
C) Keras for handling sparse retrieval and pre-trained generation models.
D) PyTorch for implementing rule-based retrieval and generative model fine-tuning.

Solutions: