NCA-GENM Question 150
Select 4An organization is training a multimodal AI model that processes both image and text data. They notice that the training process consumes significant energy and suspect the model's efficiency could be improved. Which approaches should they prioritize to optimize the model for energy efficiency while maintaining trustworthiness and accuracy?
- A
Implement transfer learning using a pre-trained model instead of training from scratch.
- B
Perform hyperparameter tuning to identify configurations that balance accuracy and resource consumption.
- C
Reduce the amount of training data to decrease computational load.
- D
Incorporate supervised training enhancements to improve task-specific accuracy.
- E
Conduct rigorous testing to ensure the model meets energy efficiency and accuracy benchmarks.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Optimizing a multimodal AI model for energy efficiency while maintaining accuracy and trustworthiness involves leveraging techniques like transfer learning, hyperparameter tuning, and supervised training enhancements. These methods minimize computational requirements and improve task-specific performance. Rigorous testing is critical to validate the effectiveness of these optimizations, ensuring the model meets predefined benchmarks. Reducing training data, however, can compromise accuracy and is not a recommended optimization method.
- A. Correct.
Using transfer learning allows the organization to leverage pre-trained models, reducing the computational cost and energy consumption of training from scratch while maintaining accuracy.
- B. Correct.
Hyperparameter tuning helps identify optimal configurations that minimize energy consumption without compromising performance, making it essential for energy-efficient models.
- C. Incorrect.
Reducing the amount of training data may decrease computational requirements but risks underfitting and degraded model accuracy, making it an unreliable optimization strategy.
- D. Correct.
Supervised training enhancements improve the model's accuracy for specific tasks, contributing to better performance without necessarily increasing energy consumption.
- E. Correct.
Rigorous testing ensures the model meets energy efficiency and accuracy benchmarks, validating the optimizations and maintaining trustworthiness.