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You have an Apache Spark cluster in Azure HDInsight. The cluster includes 200 TB in five Apache Hive tables that have multiple foreign key relationships. You have an Azure Machine Learning model that was built by using SPARK Accelerated Failure Time (AFT) Survival Regression Model (spark.survreg). You need to prepare the Hive data into a single table as input for the Machine Learning model. The Hive data must be prepared in the least amount of time possible. What should you use to prepare the data?
Correct Answer: A
Explanation/Reference: Explanation: Create features for data in an HDInsight Hadoop cluster using Hive queries. Feature engineering attempts to increase the predictive power of learning algorithms by creating features from raw data that facilitate the learning process. You can run HiveQL queries from Azure ML, and access data processed in Hive and stored in blob storage, by using the Import Data module. References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-machine-learning-overview#azure- machine-learning-and-hive
