Note: This question is part of a series of questions that use the same scenario. For your convenience, the
scenario is repeated in each question. Each question presents a different goal and answer choices, but the text
of the scenario is exactly the same in each question in this series.
You plan to create a predictive analytics solution for credit risk assessment and fraud prediction in Azure
Machine Learning. The Machine Learning workspace for the solution will be shared with other users in your
organization. You will add assets to projects and conduct experiments in the workspace.
The experiments will be used for training models that will be published to provide scoring from web services.
The experiment for fraud prediction will use Machine Learning modules and APIs to train the models and will
predict probabilities in an Apache Hadoop ecosystem.
You need to alter the list of columns that will be used for predicting fraud for an input web service endpoint. The
columns from the original data source must be retained while running the Machine Learning experiment.
Which module should you add after the web service input module and before the prediction module?
A.
Edit Metadata
B.
Import Data
C.
SMOTE
D.
Select Columns in Dataset