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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 plan to configure the resources for part of a workflow that will be used to preprocess data from files stored in Azure Blob storage. You plan to use Python to preprocess and store the data in Hadoop.
You need to get the data into Hadoop as quickly as possible.
Which three actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Create an Azure virtual machine (VM), and then configure MapReduce on the VM.
B. Create an Azure HDInsight Hadoop cluster.
C. Create an Azure virtual machine (VM), and then install an IPython Notebook server.
D. Process the files by using Python to store the data to a Hadoop instance.
E. Create the Machine learning experiment, and then add an Execute Python Script module.

Answer: BDE

Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You need to use only one percent of an Apache Hive data table by conducting random sampling by groups.
Which module should you use?

A. Execute Python Script
B. Tune Model Hyperparameters
C. Normalize Data
D. Select Columns in Dataset
E. Import Data
F. Edit Metadata
G. Clip Values
H. Clean Missing Data

Answer: A

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?

A. Collaborative filtering
B. Boosted Decision Tree Regression model
C. Two-Class boosted decision tree
D. K-Means Clustering

Answer: A

You plan to use Azure Machine Learning to develop a predictive model.
You plan to include an Execute Python Script module.
What capability does the module provide?

A. Outputting a file to a network location.
B. Performing interactive debugging of a Python script.
C. Saving the results of a Python script run in a Machine Learning environment to a local file.
D. Visualizing univariate and multivariate summaries by using Python code.

Answer: D

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?

A. overfitting
B. generalization
C. underfitting
D. a simple predictor

Answer: A


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