Google Cloud Certified Professional Data Engineer Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose C1

  The model fits the training data well but performs poorly on new data → this is overfitting, especially with a large/deep network. Dropout is a regularization technique that randomly deactivates neurons during training, reduces co-adaptation, and improves generalization. Threading only affects runtime, Serialization is for saving models, and Dimensionality Reduction isn’t the primary fix here since the model already fits the training set well. Therefore: C. Dropout Methods.

• Chose C1

  Only option which fix overfitting

• Chose C1

  Answer is C. For Neural Networks Dropout method of regularization thecnique, helps to prevent the overfit of the model, the main idea is remove/turn/off random % of neurons at training.