Exam Professional Machine Learning Engineer topic 1 question 71 discussion

Went with B

Went with B

Ans B. C --> No early stopping means longer training time D --> RMSLE metric need non-negative Y values

B and C is the most likely because of regression approach, But RMSLE it not allow you to take negative label to train as https://cloud.google.com/automl-tables/docs/evaluate#evaluation_metrics_for_regression_models RMSLE: The root-mean-squared logarithmic error metric is similar to RMSE, except that it uses the natural logarithm of the predicted and actual values plus 1. RMSLE penalizes under-prediction more heavily than over-prediction. It can also be a good metric when you don't want to penalize differences for large prediction values more heavily than for small prediction values. This metric ranges from zero to infinity; a lower value indicates a higher quality model. The RMSLE evaluation metric is returned only if all label and predicted values are non-negative.

BQML XGBoost ==> you have to take sql knowlege to write statement and B didn't mention how to get mx performance. Meanwhile AutoML you just click and select, click and select, click and select to get it done. and D refers to measurement to get maximizing model performance. you can minimize effort literally

I recommend option D, Use AutoML Tables to train the model with RMSLE as the optimization objective. Using AutoML Tables to train the model can be a convenient and efficient way to minimize effort and training time while still maximizing model performance. In this case, using RMSLE as the optimization objective can be a good choice because it is a good fit for regression models with negative values in the target variable.

B is correct

Its seen B for me

B is correct

It's B.

B. BigQuery is a keyword for me