Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Professional-Data-Engineer Q424: Your company built a TensorFlow neutral-network model with a large nu…

Answer is C. Bad performance of a model is either due to lack of relationship between dependent and independent variables used, or just overfit due to having used too many features and/or bad features. A: Threading parallelisation can reduce training time, but if the selected featuers are the same then the resulting performance won't have changed B: Serialization is only changing data into byte streams. This won't be useful. C: This can show which features are bad. E.g. if it is one feature causing bad performance, then the dropout method will show it, so you can remove it from the model and retrain it. D: This would become clear if the model did not fit the training data well. But the question says that the model fits the training data well, so D is not the answer.