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As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A. Use the batch prediction functionality of AI Platform.

B. Create a serving pipeline in Compute Engine for prediction.

C. Use Cloud Functions for prediction each time a new data point is ingested.

D. Deploy the model on AI Platform and create a version of it for online inference.

You are working on a classification problem with time series data. After conducting just a few experiments using random cross-validation, you achieved an Area Under the Receiver Operating Characteristic Curve (AUC ROC) value of 99% on the training data. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

A. Address the model overfitting by using a less complex algorithm and use k-fold cross-validation.

B. Address data leakage by applying nested cross-validation during model training.

C. Address data leakage by removing features highly correlated with the target value.

D. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metric would give you the most confidence in your model?

A. Precision

B. Recall

C. RMSE

D. F1 score