Topic 1 Question 7
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
Configure AutoML Tables to perform the classification task.
Run a BigQuery ML task to perform logistic regression for the classification.
Use AI Platform Notebooks to run the classification model with pandas library.
Use AI Platform to run the classification model job configured for hyperparameter tuning.
解説
BigQuery ML supports supervised learningג€ with the logistic regression model type. Reference: https://cloud.google.com/bigquery-ml/docs/logistic-regression-prediction
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コメント(17)
A. Because BigQuery ML need to write code.
👍 23guruguru2021/07/23The answer is B. Automl Tables can't do Hyperparameter Tuning
👍 4chohan2021/06/15=New Question7= You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?
A. Build your custom container to run jobs on Al Platform Training B. Use a built-in model available on Al Platform Training C. Build your custom containers to run distributed training jobs on Al Platform Training D. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
👍 3MisterHairy2021/12/22
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