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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. A generative AI model is given the following prompt: "Translate the following sentence into French: 'The sun is shining brightly today.'" No additional context or examples are provided.
This is an example of which type of prompting and why is it likely to succeed?

A) Zero-shot prompting, but it will only succeed if the prompt includes a few additional translation examples.
B) Zero-shot prompting, because the model is expected to perform the task without any example.
C) Zero-shot prompting, but it will likely fail since translation tasks always need examples for accuracy.
D) Few-shot prompting, because the task requires translation examples to guide the model.

2. In the context of Generative AI, which of the following is an appropriate method for setting a stopping criterion to prevent a model from generating excessively long sequences?

A) Setting a maximum token length for the output sequence
B) Allowing the model to continue until it reaches the minimum token length specified
C) Stopping the generation when the model reaches the end of its training dataset
D) Stopping the model based on the number of unique tokens generated

3. You are optimizing a Generative AI model for a business application where cost savings are a priority.
Which of the following modifications to the model's parameters will most effectively reduce the overall generation cost while minimizing the loss of output quality?

A) Set a lower value for the top-p (nucleus sampling) parameter.
B) Decrease the model's context window size.
C) Set a lower value for the frequency penalty parameter.
D) Reduce the number of layers in the neural network during inference.

4. 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) Running evaluation metrics on the baseline model to measure initial performance.
D) Defining task-specific instructions and fine-tuning them through prompt design.

5. Which quantization technique aims to optimize a model by converting weights and activations into 8-bit integers while minimizing the impact on the model's performance?

A) Post-training dynamic quantization
B) Hybrid quantization
C) Post-training static quantization
D) Quantization-aware training (QAT)

Solutions: