Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test

Correct Answer: B and C

Overfitting happens when a model performs well on a training set, generating only a small error while giving wrong output for the test set. This happens because the model is only picking up specific features input found in the training set instead of picking out the general features of the given training set.

To solve overfitting, the following would help in improving the model’s quality:

• Increase the number of examples, the more data a model is trained with, the more use cases the model can be training on and better improves its predictions.
• Tune hyperparameters which are related to number and size of hidden layers (for neural networks), and regularization, which means using techniques to make your model simpler such as using dropout method to remove neuron networks or adding “penalty” parameters to the cost function.
• Remove features by removing irrelevant features. Feature engineering is a wide subject and feature selection is a critical part of building and training a model. Some algorithms have built-in feature selection, but in some cases, data scientists need to cherry-pick or manually select or remove features for debugging and finding the best model output.
                                                                       
  From the brief explanation, to solve the overfitting problem in the scenario, you need to:
• Increase the training set.
• Decrease features parameters.
• Increase regularization.

Source(s):
Building a serverless Machine learning model:

https://cloud.google.com/solutions/building-a- serverless-ml-model