Google Cloud Professional Data Engineer — Question 275

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?

Answer options

• A. Threading
• B. Serialization
• C. Dropout Methods
• D. Dimensionality Reduction

Correct answer: C

Explanation

The correct answer is C, Dropout Methods, which help prevent overfitting by randomly deactivating neurons during training, promoting a more generalized model. Options A (Threading) and B (Serialization) are unrelated to model performance and do not address overfitting. Option D (Dimensionality Reduction) can reduce complexity but does not specifically target overfitting like dropout does.