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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. 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) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
B) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
C) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
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.

2. You are preparing a dataset to fine-tune a language model for sentiment analysis. The dataset consists of user reviews with a mix of neutral, positive, and negative sentiments.
Which of the following strategies will best ensure that the model learns balanced sentiment detection?

A) Ensure an equal distribution of positive, negative, and neutral sentiment examples
B) Increase the number of positive sentiment examples in the dataset
C) Focus only on neutral and negative examples to challenge the model
D) Convert neutral examples into either positive or negative to simplify the task

3. While developing a Retrieval-Augmented Generation (RAG) system using the transformers library, you want to improve the retrieval quality by ensuring that your queries and documents are represented in the same latent space for effective similarity matching.
Which of the following techniques would be the most appropriate to ensure this alignment between queries and documents?

A) Use different transformer models for documents and queries, and normalize their embeddings to align them in the same latent space.
B) Use a randomly initialized transformer model to encode both documents and queries for unbiased similarity calculation.
C) Use a pre-trained BERT model to encode the documents and a pre-trained GPT model to encode the queries, ensuring diversity in embeddings.
D) Fine-tune a transformer model on a document-query similarity task, so that both queries and documents are encoded into the same vector space for retrieval.

4. When optimizing the tuning process in IBM watsonx Tuning Studio, what is the main purpose of fine-tuning metering options?

A) To enforce stricter thresholds for model accuracy in order to ensure high precision.
B) To monitor the effectiveness of early stopping techniques applied during training.
C) To track the computational cost and time for fine-tuning models based on resource consumption.
D) To limit the number of iterations allowed for model training.

5. You are tasked with reconstructing a prompt used in an AI-based customer support chatbot. The current prompt generates lengthy, detailed answers that are often overly verbose and unnecessary for the customer's inquiries. Your objective is to optimize this prompt to reduce model usage costs without compromising the quality of the responses.
Which of the following strategies is the most effective in reducing the cost of using a Generative AI model while maintaining response relevance and clarity?

A) Splitting the prompt into multiple sub-prompts to generate responses for different sections separately.
B) Reducing the token limit in the model configuration to restrict the length of responses.
C) Revising the prompt to make it more specific by narrowing the scope of expected responses.
D) Using temperature scaling to increase randomness and reduce token usage.

Solutions: