Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stock broking businesses as well as unit trust and asset administration, having served the financial community over the past five decades.
AFS being one the largest banks in the region is planning to improve its segment business by launching a campaign to identify potential customers for their product(s) based on their past behavior? AFS is looking for services that address batch and real-time predictive analytics and also the relevant model to solve customer problem. Which of the following options in combination help us in efficiently solve the problem. Select 2 options.

Answer options:

A.Amazon ML for batch analytics and SPARK on EMR for real-time analytics
B.Binary classification model
C.Multiclass classification model
D.Regression Model
E.Amazon ML for both batch and real-time analytics

Answer : B and E
Option A is incorrect. Machine Learning web service offers 2 types of predictions
 Batch Predictions asynchronously generate predictions for multiple input data observations
 Real-time Predictions synchronously generate predictions for individual data observations
https://docs.aws.amazon.com/machine-learning/latest/dg/amazon-machine-learning-key-concepts.html
Option B is correct. Problem being a binary classification whether the customer will subscribe to new product or not ML models for binary classification predict a binary outcome. To train binary classification models, Amazon ML uses the industry-standard learning algorithm known as logistic regression. Amazon ML provides an industry-standard accuracy metric for binary classification models called Area Under the (Receiver Operating Characteristic) Curve (AUC)
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html
Option C is incorrect. ML models for multiclass classification problems allow you to generate predictions for multiple classes. In Amazon ML, the macro-average F1 score is used to evaluate the predictive accuracy of a multiclass metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html
Option D is incorrect. ML models for regression problems predict a numeric value. Amazon ML uses the industry standard root mean square error (RMSE) metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.html
Option E is correct. Machine Learning offers 2 types of predictions
Batch Predictions asynchronously generate predictions for multiple input data observations
Real-time Predictions synchronously generate predictions for individual data observations
https://docs.aws.amazon.com/machine-learning/latest/dg/amazon-machine-learning-key-concepts.html