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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with fine-tuning a language model using a prompt-tuning approach on a dataset consisting of customer service chat logs. The goal is to optimize the model's ability to generate polite and contextually appropriate responses.
Which of the following steps are essential when preparing the dataset for prompt-tuning in this context? (Select two)

A) Ensure each conversation includes both customer input and agent response as context for the model.
B) Remove any conversations that contain excessive user slang or misspellings.
C) Separate the dataset into training, validation, and test subsets.
D) Ensure all examples in the dataset follow the exact same input-output format.
E) Convert all user queries into lowercase to reduce noise in the dataset.

2. In a Retrieval-Augmented Generation (RAG) system designed for technical document retrieval, you are tasked with implementing text chunking techniques using the LangChain library. The technical documents are large and contain numerous tables, figures, and bullet points.
What is the most effective way to handle text splitting to ensure high-quality retrieval?

A) Use a hybrid approach, splitting the text by both semantic boundaries (like paragraphs) and content-specific markers (like bullet points and tables), while keeping chunks within the model's token limit.
B) Convert tables and figures into plain text and split the document by character count to maintain even chunk sizes.
C) Split the text into equal-sized chunks of 512 characters, regardless of the content structure, to improve consistency in retrieval.
D) Split the text only at paragraph breaks, ignoring tables and figures, as they can be processed separately.

3. When working with IBM Watsonx Generative AI models, it's important to configure proper stopping criteria to control when the model should terminate the text generation process. You are developing a chatbot where responses should stay within a manageable length without losing coherence.
Which configuration best represents an effective stopping criterion to ensure coherent responses without abrupt truncation?

A) Greedy decoding with maximum tokens set to 20 and a stop sequence of "END".
B) Greedy decoding with no stop sequence and maximum tokens set to 200.
C) Beam search decoding with a stop sequence of "END" and a maximum tokens limit of 50.
D) Greedy decoding with temperature set to 2.0 and no stop sequence.

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 reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
B) 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.
C) 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.
D) Quantization can increase the inference time of a model since it adds computational complexity when converting from higher to lower precision formats during runtime.
E) Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.

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

Solutions: