You are analyzing taxi trips in New York City. You leverage the Azure Data Factory to create data pipelines and to orchestrate data movement.You plan to develop a predictive model for 170 million rows (37 GB) of raw data in Apache Hive by using Microsoft R Server to identify which factors contribute to the passenger tipping behavior.All of the platforms that are used for the analysis are the same. Each worker node has eight processor cores and 26 GB of memory.Which type of Azure HDInsight cluster should you use to produce results as quickly as possible?
You plan to use the Data Science Virtual Machine for development, but you are unfamiliar with R scripts.You need to generate R code for an experiment.Which IDE should you use?
HOTSPOT -You need to use R code in a Transact-SQL statement to merge the repeating values 1 through 6 with Col1 in a table.Which statement should you use? To answer, select the appropriate options in the answer area.NOTE: Each correct selection is worth one point.Hot Area:
You are performing exploratory analysis of files that are encoded in a complex proprietary format. The format requires disk intensive access to several dependent files in HDFS.You need to build an Azure Machine Learning model by using a canopy clustering algorithm. You must ensure that changes to proprietary file formats can be maintained by using the least amount of effort.Which Machine Learning library should you use?
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.
DRAG DROP -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 finish training the model and are ready to publish a predictive web service that will provide the users with the ability to specify the data source and the save location of the results. The model includes a Split Data module.Which two actions should you perform to convert the Machine Learning experiment to a predictive web service? To answer, drag the appropriate actions to the correct targets. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.NOTE: Each correct selection is worth one point.Select and Place:
