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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. During the fine-tuning of a large language model (LLM) with InstructLab for a legal document classification task, you notice that the model performs exceptionally well on the training set but poorly on the validation set.
What could be done to address the overfitting issue and improve the model's generalization? (Select two)

A) Introduce dropout regularization during fine-tuning to prevent overfitting.
B) Increase the size of the model to better capture complex patterns in the training data.
C) Remove all regularization and fine-tune the model on the training set until convergence.
D) Use early stopping based on the validation set performance.
E) Fine-tune the model for more epochs to ensure the model has fully learned from the training data.

2. You are implementing a few-shot prompting strategy with IBM Watsonx to improve the model's performance in generating customer service responses. The goal is to ensure the model understands the tone and format required for polite and concise replies.
Which of the following strategies best illustrates the correct way to use few-shot prompting?

A) Use only negative examples in the prompt to show the model what not to generate in terms of tone and format.
B) Include a large number of examples, typically over 10, in the input prompt to ensure the model learns from diverse cases.
C) Provide one or two well-structured examples that demonstrate the expected tone and format of the customer service responses within the prompt.
D) Provide example prompts with multiple different output styles to give the model a range of responses to choose from.

3. In the context of Retrieval-Augmented Generation (RAG), embeddings play a crucial role in ensuring relevant information is retrieved to augment the generative AI's response.
Which of the following best describes the role of embeddings in the RAG process?

A) Embeddings represent the search space for the retriever model, allowing the system to retrieve semantically relevant information based on input queries.
B) Embeddings are pre-trained generative models that augment the retrieval step by generating new query variations.
C) Embeddings are only used in fine-tuning generative models and play no role in the retrieval process.
D) Embeddings are used to directly generate the textual responses in the output.

4. A generative AI model designed for healthcare content generation is being evaluated for ethical risks. The model tends to give preference to certain demographic groups when recommending treatments.
What is the most effective method to identify and mitigate this bias during the prompt engineering phase?

A) Train the model on a smaller dataset that excludes demographic information, to remove bias from its learned patterns.
B) Limit the model's context window to prevent it from over-relying on demographic information.
C) Use adversarial debiasing techniques to adjust the model's internal representations during training.
D) Adjust the temperature to 1.0 to ensure the model generates more balanced and less biased outputs.

5. Your team is responsible for deploying a generative AI system that will interact with customers through automated chatbots. To improve the quality and consistency of responses across different queries and customer profiles, the team has developed several prompt templates. These templates aim to standardize input to the model, ensuring that outputs are aligned with business objectives. However, the team is debating whether using these prompt templates will provide tangible benefits in the deployment.
What is the primary benefit of deploying prompt templates in this AI system?

A) Improving the scalability of the system by allowing the model to handle more diverse inputs without requiring additional fine-tuning.
B) Reducing the overall inference time by streamlining the input-output process for the model, ensuring faster responses.
C) Enhancing the model's ability to generalize across unseen data by training it specifically on the variations included in the prompt template.
D) Enabling more predictable and consistent outputs across different inputs, aligning the model's responses more closely with the business goals.

Solutions: