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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. In the lifecycle of deploying a prompt template for a generative AI solution, which of the following best describes the stage where user feedback is integrated to refine the template's performance?

A) Iterative prompt tuning based on A/B test results and feedback loops
B) Initial testing on synthetic datasets and model validation
C) Deployment to production with regular monitoring and logging
D) Retraining the model based on emerging trends in data

2. Which of the following decoding strategies would most likely result in generating creative and diverse text outputs while minimizing repetition, when using a generative AI model?

A) Nucleus Sampling (Top-p) with p = 0.9
B) Beam Search Decoding with a small beam size (e.g., 2)
C) Greedy Decoding
D) Temperature Sampling with temperature = 0.0

3. You are reviewing the results of a prompt-tuning experiment where the goal was to improve an LLM's ability to summarize technical documentation. Upon inspecting the experiment results, you notice that the model has a high recall but relatively low precision.
What does this likely indicate about the model's performance, and how should you approach further tuning?

A) The model is overly conservative, missing relevant details; focus on improving recall.
B) The model's summaries are incomplete, indicating poor understanding of the source material; consider fine-tuning the pre-trained embeddings.
C) The model's length of generated summaries is too short, indicating underfitting.
D) The model is generating too many irrelevant details; focus on improving precision.

4. What is a key advantage of using prompt variables in IBM Watsonx for a chatbot application that needs to handle multiple user intents?

A) Using prompt variables ensures that the model will always select the most relevant response based on past conversations.
B) Prompt variables allow for the dynamic injection of user-specific details into the response, improving personalization.
C) Prompt variables enable developers to predefine multiple static responses for each possible user input.
D) Prompt variables allow the model to automatically detect the user's intent, ensuring accurate responses.

5. You are tasked with fine-tuning a pre-trained large language model (LLM) using synthetic data generated through the IBM watsonx user interface.
Which of the following steps should you follow to ensure the model is fine-tuned correctly and the synthetic data is used effectively?

A) Directly upload synthetic data without inspecting or validating it and initiate the fine-tuning process.
B) Select the pre-trained model, generate synthetic data, inspect the generated data for quality, and fine-tune the model by adjusting hyperparameters and training settings.
C) Use synthetic data as a replacement for real-world data without cross-validation or any quality control measures.
D) Select the pre-trained model, generate synthetic data, and fine-tune the model using default parameters without further customization.

Solutions: