Databricks Generative AI Engineer Associate Question 163
Single answerYou are tasked with selecting a model from a model hub to perform sentiment analysis on customer reviews. The model should be lightweight enough to deploy on edge devices and must be fine-tuned for sentiment analysis tasks. Based on the following model metadata, which model is the most appropriate choice?
- A
Model A: A general-purpose language model with 1.5 billion parameters, trained on diverse datasets, but not fine-tuned for sentiment analysis.
- B
Model B: A lightweight model with 50 million parameters, explicitly fine-tuned for sentiment analysis, and optimized for edge deployment.
- C
Model C: A large-scale multilingual model with 6 billion parameters, fine-tuned for translation tasks but not for sentiment analysis.
- D
Model D: A lightweight model with 60 million parameters, not specialized for any downstream task and trained only on general text datasets.
Show answer and explanation
Correct answer: B
Explanation
Model B is the most suitable choice because it meets both requirements: being lightweight for edge deployment and being fine-tuned for the specific task of sentiment analysis. The other models either lack task-specific fine-tuning or are too large for edge deployment.
- A. Incorrect.
Model A has a large number of parameters, making it unsuitable for edge deployment. Additionally, it is not fine-tuned for sentiment analysis, which is a key requirement.
- B. Correct.
Model B is lightweight with only 50 million parameters, making it ideal for edge deployment. It is also fine-tuned for sentiment analysis, making it the best choice for the task.
- C. Incorrect.
Model C has a large number of parameters, making it unsuitable for edge deployment. Furthermore, it is fine-tuned for translation tasks, not sentiment analysis.
- D. Incorrect.
Model D is lightweight, but it is not fine-tuned for any specific task, including sentiment analysis, making it unsuitable for this task.