An industrial company is leveraging a custom deep neural network model developed with TensorFlow to enhance its quality control system. The model is designed to identify semi-finished products that should be discarded, using images captured from various stages of the production line. Despite the model showing convergence during training, the performance on the test set is not meeting expectations. The company is constrained by a tight budget and requires a solution that is scalable across multiple production lines. Given these constraints, which of the following strategies would most effectively address the model's performance issues? (Choose 3 options)

Real Exam

Augmenting the training dataset with additional images to improve the model's ability to generalize.

26.8%

Replacing the deep neural network with a simpler model like XGBoost to reduce complexity and potential overfitting.

3.7%

Implementing dropout layers or L2 regularization within the neural network to combat overfitting.

42.7%

Decreasing the learning rate to ensure more precise weight updates during training.

3.7%

Simplifying the neural network architecture to reduce overfitting while maintaining the model's ability to learn from the data.

23.2%

Applying L2 Ridge Regression to the model's output layer to penalize large weights.

Google Professional Machine Learning Engineer