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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)

A) Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.
B) Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
C) Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
D) Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
E) Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.

2. Which of the following statements accurately describes a drawback of using soft prompts in generative AI model optimization?

A) Soft prompts make it easier to control the model's behavior as the prompts are flexible and can be adjusted by the user during inference.
B) Soft prompts can increase the model's interpretability by providing clear, user-defined input instructions.
C) Soft prompts require additional computational resources during training, which can limit their scalability in real-time applications.
D) Soft prompts offer improved performance for specific tasks but are harder to implement when fine-tuning models across multiple domains.

3. You are generating product descriptions for an online marketplace using a generative AI model. The output is coherent but tends to repeat the same phrases and words excessively. You decide to apply a repetition penalty to reduce this repetition while keeping the temperature set to a value that maintains creativity in the text generation.
Which of the following adjustments would best achieve this goal?

A) Set repetition penalty to 1.0 and decrease temperature from 0.8 to 0.3
B) Set repetition penalty to 2.0 and increase temperature from 0.7 to 1.2
C) Set repetition penalty to 0.0
D) Set repetition penalty to 1.5 and maintain temperature at 0.8

4. You are developing a machine learning pipeline using IBM watsonx that includes fine-tuning an LLM with a dataset containing sensitive personal information. To ensure privacy, you decide to apply differential privacy.
Which of the following actions is most critical to configure in the user interface to meet the differential privacy requirements during model fine-tuning?

A) Use synthetic data only, which eliminates the need for differential privacy as it does not contain real user information.
B) Remove differential privacy settings for fine-tuning, but apply them in the final inference model to reduce performance degradation.
C) Apply a differential privacy mechanism that adds calibrated noise to both the model updates and synthetic data generation process.
D) Increase the learning rate and batch size to maximize the noise added by differential privacy algorithms.

5. You have been assigned the task of fine-tuning a large language model (LLM) for a chatbot that will assist users with technical troubleshooting. The goal is to ensure the chatbot responds accurately to user queries, but also in a specific tone and format.
Which of the following steps is the first critical phase in the InstructLab workflow to ensure successful customization of the model?

A) Pre-processing and augmenting the training data to improve the model's generalization capabilities.
B) Deploying the model in a real-time environment for user feedback collection.
C) Defining task-specific instructions and fine-tuning them through prompt design.
D) Running evaluation metrics on the baseline model to measure initial performance.

Solutions: