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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are developing a Retrieval-Augmented Generation (RAG) system to enhance the responses of a legal chatbot by integrating it with a vast legal document repository. You are using LangChain to build the pipeline, Watson ML for model hosting, and Elasticsearch as your document store.
What would be the most appropriate approach for combining these components into a RAG pipeline?

A) Use Watson ML for document retrieval and response generation -> Use Elasticsearch to store model responses -> Use LangChain for chaining the responses together.
B) Use LangChain to chain together query encoding, document retrieval from Elasticsearch, and Watson ML for response generation.
C) Use Elasticsearch for document retrieval -> Use LangChain to encode the documents -> Generate the response using Watson ML.
D) Use LangChain to pre-process documents -> Use Elasticsearch for model storage -> Use Watson ML to retrieve documents and generate responses.

2. 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 50% of the original training data to ensure the fine-tuned model generalizes well across both new and existing tasks.
B) Use no more than 100 examples per task to avoid overwhelming the model's general capabilities with task-specific data.
C) 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.
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.

3. When generating data for prompt tuning in IBM watsonx, which of the following is the most effective method for ensuring that the model can generalize well to a variety of tasks?

A) Focus on generating prompts specific to a single domain to train the model on specialized tasks.
B) Generate a single highly-detailed prompt that covers all potential use cases to maximize generalization.
C) Use a diverse set of prompts covering multiple task domains with varying levels of complexity.
D) Prioritize prompts with repetitive patterns to help the model memorize key responses.

4. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
B) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
C) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
D) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.
E) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.

5. 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) 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 reduces the precision of model weights and activations, allowing for lower memory usage and faster computation with minimal impact on model accuracy.
E) Quantization-aware training (QAT) can help mitigate the accuracy degradation that occurs during quantization by simulating lower precision during the training process.

Solutions: