Exam Professional Data Engineer topic 1 question 2 discussion

I think it should be B because we have to use a combination of old and new test data as well as training data

B, as we need to train the data with new data, so that it will keep learning, and as well as used for test

Answer is B, need to take acount both data to train

We should continuously retrain existing data with latest data to balance the need to adapt to new changes while not overriding historical knowledge. This is called Continuous Retraining where the model is periodically updated with latest data so that recommendations remain accurate over time.

Continuously retrain the model on a combination of existing data and the new data.

From my point of view, we should take both datasets.

Option A is not recommended because retraining the model on just new data will cause the model to lose the information it has learned from the historical data. Option C and D are not recommended because they are using the new data as test set and this approach will lead to a model that is overfitting and not generalize well to new users. So answer is B

Answer is C. It is time sensitive data so latest data should be used for testing. Reference: https://cloud.google.com/automl-tables/docs/prepare#ml-use

This approach allows the model to benefit from both the historical data (existing data) and the new data, ensuring that it adapts to changing preferences while retaining knowledge from the past. By combining both types of data, the model can learn to make recommendations that are up-to-date and relevant to users' evolving preferences.

train on old and new data

Option B is the right answer. Since the questions states the models needs to be updated since the clothing preference changes. Hence we need the new data to be utilized for training/ updating model.

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there are two point first when retraining second what data. I think retraining should be occur when the model could not predict well in this case there is monitoring metric should be needed first but no one said, second what data? in this case I think the answer is A. because when the model could not predict well it means the data variance and bias are changed so, it's no make sense what is combination new data with old data because the data being not be changed is not necessary anymore..

The trend keep changing, so must mix new and old data...

B. Continuously retrain the model on a combination of existing data and the new data. This approach will help to ensure that the model remains up-to-date with the latest fashion preferences of the users, while also leveraging the historical data to provide context and improve the accuracy of the recommendations. Retraining the model on a combination of existing and new data will help to prevent the model from being overly influenced by the new data and losing its ability to generalize to users with different preferences. Option A is not recommended because retraining the model on just new data will cause the model to lose the information it has learned from the historical data. Option C and D are not recommended because they are using the new data as test set and this approach will lead to a model that is overfitting and not generalize well to new users.

The answer can be A, if we implement online learning! But for regular model which can't implement online learning (everything with no gradient descent) the answer should be B.

Correct answer is B