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IBM watsonx Generative AI Engineer - Associate Sample Questions:

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

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

2. When optimizing a prompt-tuned model, which parameter adjustment would most likely help prevent overfitting without negatively impacting the model's ability to generalize to unseen prompts?

A) Removing weight decay entirely
B) Increasing the model's learning rate
C) Reducing the model's learning rate
D) Increasing the model's dropout rate

3. In the context of large-scale synthetic data generation for fine-tuning a generative AI model, which of the following practices can lead to data that effectively improves the model's performance on downstream tasks?

A) Using domain-specific templates to generate synthetic data that reflects the target use case
B) Generating synthetic data that overrepresents the simplest task cases to reduce computational load
C) Avoiding any post-processing or filtering of synthetic data to retain diversity
D) Randomly generating sentences without adhering to the task-specific instructions

4. During prompt engineering for IBM Watsonx, you need to understand how the decoding process works when generating responses.
Which of the following best describes a high-level overview of the decoding process in generative AI?

A) Decoding occurs only in reinforcement learning, where the model refines its responses based on user feedback over multiple generations.
B) Decoding is the process where the model generates a response token-by-token, choosing each token based on the probability distribution over all possible tokens.
C) Decoding involves translating the input data into a format that the AI model can understand before generating an output.
D) Decoding is the final step in training, where the AI model verifies the accuracy of its outputs against a predefined set of labels.

5. While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?

A) Using stop tokens early in the prompt to minimize generation length.
B) Switching from a narrative-style prompt to a bulleted list format.
C) Including multiple tasks in a single prompt to maximize efficiency.
D) Breaking complex prompts into simpler, sequential prompts.

Solutions: