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 are implementing a batch processing solution by using Azure HDInsight.You have a table that contains sales data.You plan to implement a query that will return the number of orders by zip code.You need to minimize the execution time of the queries and to maximize the compression level of the resulting data.What should you do?
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 are implementing a batch processing solution by using Azure HDInsight.You have data stored in Azure.You need to ensure that you can access the data by using Azure Active Directory (Azure AD) identities.What should you do?
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 are implementing a batch processing solution by using Azure HDInsight.You plan to import 300 TB of data.You plan to use one job that has many concurrent tasks to import the data in memory.You need to maximize the amount of concurrent tasks for the job.What should you do?
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 are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster has 24 processor cores and 512 GB of memory.The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)The architecture will be used by the following users:✑ Support analysts who run applications that will use REST to submit Spark jobs.✑ Business analysts who use JDBC and ODBC client applications from a real-time view. The business analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced by subsequent queries.✑ Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of the notebooks must support native interpreters for data sources that are batch processed. The serving layer queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the data sources, which allow the data analysts to use Spark SQL.The data sources in the batch layer share a common storage container. The following data sources are used:✑ Hive for sales data✑ Apache HBase for operations data✑ HBase for logistics data by using a single region serverYou need to ensure that the analysts can query the logistics data by using JDBC APIs and SQL APIs.Which technology should you implement?
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 are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster has 24 processor cores and 512 GB of memory.The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)The architecture will be used by the following users:✑ Support analysts who run applications that will use REST to submit Spark jobs.✑ Business analysts who use JDBC and ODBC client applications from a real-time view. The business analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced by subsequent queries.✑ Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of the notebooks must support native interpreters for data sources that are batch processed. The serving layer queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the data sources, which allow the data analysts to use Spark SQL.The data sources in the batch layer share a common storage container. The following data sources are used:✑ Hive for sales data✑ Apache HBase for operations data✑ HBase for logistics data by using a single region serverThe business analysts report that they experience performance issues when they run the monitoring queries.You troubleshoot the performance issues and discover that the intermediate tables generated when the analysts run the queries cause pressure for the JavaVirtual Machine (JVM) garbage collection per job.Which configuration settings should you modify to alleviate the performance issues?
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 are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster has 24 processor cores and 512 GB of memory.The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)The architecture will be used by the following users:✑ Support analysts who run applications that will use REST to submit Spark jobs.✑ Business analysts who use JDBC and ODBC client applications from a real-time view. The business analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced by subsequent queries.✑ Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of the notebooks must support native interpreters for data sources that are batch processed. The serving layer queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the data sources, which allow the data analysts to use Spark SQL.The data sources in the batch layer share a common storage container. The following data sources are used:✑ Hive for sales data✑ Apache HBase for operations data✑ HBase for logistics data by using a single region serverThe business analysts require queries to monitor the sales data. The queries must be faster and more interactive than the batch layer queries.You need to create a new infrastructure to support the queries. The solution must ensure that you can tune the cache policies of the queries.Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.Select and Place:
