Microsoft 70-774 Exam Questions

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?

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You have an Azure Machine Learning experiment.

You discover that a model causes many errors in a production dataset. The model causes only few errors in the training data.

What is the cause of the errors?

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You are building an Azure Machine Learning solution for an online retailer.

When a customer selects a product, you need to recommend products that the customer might like to purchase at the same time. The recommendation should be based on what other customers purchased when they purchased the same product.

Which model should you use?

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You need to integrate code and formatted text into an Azure Machine Learning experiment that enables interactive execution.

What should you use?

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You have a dataset that is missing values in a column named Column3. Column3 is correlated to two columns named Column4 and Column5.

You need to improve the accuracy of the dataset, while minimizing data loss.

What should you do?

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