Microsoft 70-775 Study Guides 2019

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Free demo questions for Microsoft 70-775 Exam Dumps Below:

NEW QUESTION 1
DRAG DROP
You have an Apache HBase cluster in Azure HDInsight. The cluster has a table named sales that contains a column family named customerfamily.
You need to add a new column family named customeraddr to the sales table.
How should you complete the command? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once or not at all.
70-775 dumps exhibit

    Answer:

    Explanation:
    Hbase > disable 'sales'
    Hbase > alter 'sales'
    ‘customerfamily’,
    {NAME => 'customeraddr',
    IN_MEMORY => false},
    Hbase > enable 'sales'

    NEW QUESTION 2
    DRAG DROP
    You have a domain joined Apache Hadoop cluster in Azure HDInsight named hdicluster. The Linux account for hdicluster is named Inxuser.
    Your Active Directory account is names user1@fabrikam.com. You need to run Hadoop commands from an SSH session.
    Which credentials should you use? To answer, drag the appropriate credentials to the correct commands. Each credential 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.
    70-775 dumps exhibit

      Answer:

      Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-linux-usessh-unix

      NEW QUESTION 3
      Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
      Start of Repeated Scenario:
      You have an initial data that contains the crime data from major cities.
      You plan to build training models from the training data. You plan to automate the process of adding more data to the training models and to training the models by using the additional data, including data that is collected in near real time. The system will be used to analyze event data gathered from many different sources. Such as Internet of things (IoT) devices, Live video surveillance, and traffic activities, and to generate predictions of an increased crime risk at a particular time and ptace.
      You have an incoming data stream from Twitter and an incoming data stream from
      Facebook. which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds.
      The data is in a key/value pair format. The value field represents a number that defines how many times a hashtag occurs within a Facebook post or how many times a tweet that contains a specific hashtag is retweeted.
      You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types tor the various tasks associated to the processing pipeline.
      End of repeated Scenario.
      You plan to consolidate all of the stream into a single timeline, even though none of the streams report events at the same interval.
      You need to aggregate the data from the feeds to align with the time interval stream. The result must be the sim of all values for each within a 10 second interval, with the keys being the hashtags.
      Which function should you use?

      • A. countByWindow
      • B. reduccByWindow
      • C. reduceByKeyAndWindow
      • D. countByValueAndWindow
      • E. updateStateByKey

      Answer: E

      NEW QUESTION 4
      You are configuring the Hive views on an Azure HDInsight cluster that is configured to use Kerberos.
      You plan to use the YARN loos to troubleshoot a query that runs against Apache Hadoop. You need to view the method, the service, and the authenticated account used to run the query. Which method call should you view in the YARN logs?

      • A. HQL
      • B. WebHDFS
      • C. HDFS C* API
      • D. Ambari REST API

      Answer: D

      NEW QUESTION 5
      You have an Azure HDlnsight cluster.
      You need to build a solution to ingest real-time streaming data into nonrelational distributed database.
      What should you use to build the solution?

      • A. Apatite Hive and Apache Kafka
      • B. Spark and Phoenix
      • C. Apache Storm and Apache HBase
      • D. Apache Pig and Apache HCatalog

      Answer: C

      NEW QUESTION 6
      DRAG DROP
      You have a text file named Data/examples/product.txt that contains product information.
      You need to create a new Apache Hive table, import the product information to the table, and then read the top 100 rows of the table.
      Which four code segments should you use in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
      70-775 dumps exhibit

        Answer:

        Explanation:
        val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)
        sqlContext.sql(“CREATE TABLE IF NOT EXISTS productid INT, productname STRING)”
        sqlContext.sql("LOAD DATA LOCAL INPATH ‘Data/examples/product.txt’ INTO TABLE
        product")
        sqlContext.sql("SELECT productid, productname FROM product LIMIT 100").collect().foreach (println)
        References: https://www.tutorialspoint.com/spark_sql/spark_sql_hive_tables.htm

        NEW QUESTION 7
        Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
        You are building a security tracking solution in Apache Kafka to parse Security logs. The Security logs record an entry each time a user attempts to access an application. Each log entry contains the IP address used to make the attempt and the country from which the attempt originated.
        You need to receive notifications when an IP address from outside of the United States is used to access the application.
        Solution: Create new topics. Create a file import process to send messages. Start the consumer and run the producer.
        Does this meet the goal?

        • A. Yes
        • B. No

        Answer: A

        NEW QUESTION 8
        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 deploy an enterprise data warehouse that will support in-memory analytics. The data warehouse must support connections that use the Microsoft Hive ODBC Driver and Beeline. The data warehouse will be managed by using Apache Ambari only.
        What should you do?

        • A. Use an Azure PowerShell script to create and configure a premium HDInsight cluster.Specify Apache Hadoop as the cluster type and use Linux as the operating system.
        • B. Use the Azure portal to create a standard HDInsight cluste
        • C. Specify Apache Spark as the cluster type and use Linux as the operating system.
        • D. Use an Azure PowerShell script to create a standard HDInsight cluste
        • E. Specify Apache HBase as the cluster type and use Windows as the operating system.
        • F. Use an Azure PowerShell script to create a standard HDInsight cluste
        • G. Specify Apache Storm as the cluster type and use Windows as the operating system.
        • H. Use an Azure PowerShell script to create a premium HDInsight cluste
        • I. Specify Apache HBase as the cluster type and use Linux as the operating system.
        • J. Use an Azure portal to create a standard HDInsight cluste
        • K. Specify Apache Interactive Hive as the cluster type and use Linux as the operating system.
        • L. Use an Azure portal to create a standard HDInsight cluste
        • M. Specify Apache HBase as the cluster type and use Linux as the operating system.

        Answer: F

        Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-useinteractive-hive

        NEW QUESTION 9
        You have an Azure HDInsight cluster.
        You need to store data in a file format that maximizes compression and increases read performance.
        Which type of file format should you use?

        • A. ORC
        • B. Apache Parquet
        • C. Apache Avro
        • D. Apache Sequence

        Answer: A

        Explanation: https://docs.microsoft.com/en-us/azure/data-factory/data-factory-supported-file-and-compression-formats

        NEW QUESTION 10
        Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
        Start of Repeated Scenario:
        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:
        70-775 dumps exhibit
        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 quires to access aggregate result for 15 minutes. The result will be referenced by subsequent quires.
        * 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 bath 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 suing a single region server.
        End of Repeated scenario.
        The 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 Java Virtual Machine (JVM) garbage collection per job.
        Which configuration settings should you modify to alleviate the performance issues?

        • A. spark.sql.inMemoryColumnarStorage.batchSize
        • B. spark.sql.broadcaseTimeout
        • C. spark.sql.files.openCostInBytes
        • D. spark.sql.shuffle.partitions

        Answer: D

        NEW QUESTION 11
        You use YARN to manage the resources for a Spark Thrift Server running on a Linux based Apache Spark cluster in Azure HDInsight.
        You discover that the cluster does not fully utilize the resources. You want to increase resource allocation. You need to increase the number of executors and the allocation of memory to the Spark Thrift Server driver.
        Which two parameters should you modify? Each correct answer presents part of the solution NOTE: Each correct selection is worth one point.

        • A. spark.dynamicAllocation.maxExecutors
        • B. spark.cores.max
        • C. spark.executor.memory
        • D. spark_thrift_cmd_opts
        • E. spark.executor.instances

        Answer: AC

        Explanation: References: https://stackoverflow.com/questions/37871194/how-to-tune-spark-executornumber-cores-and-executor-memory

        NEW QUESTION 12
        You have on Apache Hive table that contains one billion rows.
        You plan to use queries that will filter the data by using the WHERE clause. The values of the columns will be known only while the data loads into a Hive table.
        You need to decrease the query runtime. What should you configure?

        • A. static partitioning
        • B. bucket sampling
        • C. parallel execution
        • D. dynamic partitioning

        Answer: C

        Explanation: References: https://www.qubole.com/blog/5-tips-for-efficient-hive-queries/

        NEW QUESTION 13
        Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
        You need to deploy an HDInsight cluster that will have a custom Apache Ambari configuration.
        The cluster will be joined to a domain and must perform the following:
        * Fast data analytics and cluster computing by using in memory processing.
        * Interactive queries and micro-batch stream processing What should you do?

        • A. Use an Azure PowerShell Script to create and configure a premium HDInsight cluste
        • B. Specify Apache Hadoop as the cluster type and use Linux as the operating System.
        • C. Use the Azure portal to create a standard HDInsight cluste
        • D. Specify Apache Spark as the cluster type and use Linux as the operating system.
        • E. Use an Azure PowerShell script to create a standard HDInsight cluste
        • F. Specify Apache HBase as the cluster type and use Windows as the operating system.
        • G. Use an Azure PowerShell script to create a standard HDInsight cluste
        • H. Specify Apache Storm as the cluster type and use Windows as the operating system.
        • I. Use an Azure PowerShell script to create a premium HDInsight cluste
        • J. Specify Apache HBase as the cluster type and use Windows as the operating system.
        • K. Use an Azure portal to create a standard HDInsight cluste
        • L. Specify Apache Interactive Hive as the cluster type and use Windows as the operating system.
        • M. Use an Azure portal to create a standard HDInsight cluste
        • N. Specify Apache HBase as the cluster type and use Windows as the operating system

        Answer: D

        NEW QUESTION 14
        Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
        Start of Repeated Scenario:
        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:
        70-775 dumps exhibit
        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 quires to access aggregate result for 15 minutes. The result will be referenced by subsequent quires.
        * 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 bath 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 suing a single region server.
        End of Repeated scenario.
        You need to ensure that the analysts can query the logistics data by using JDBC APIs and SQL APIs. Which technology should you implement?

        • A. Apache Phoenix
        • B. Apache Spark
        • C. Apache Storm
        • D. Apache Hive

        Answer: D

        NEW QUESTION 15
        DRAG DROP
        Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
        Start of Repeated Scenario:
        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:
        70-775 dumps exhibit
        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 quires to access aggregate result for 15 minutes. The result will be referenced by subsequent quires.
        * 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 bath 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 suing a single region server.
        End of Repeated scenario.
        The business analysts require 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 answer area.
        70-775 dumps exhibit

          Answer:

          Explanation: 70-775 dumps exhibit

          NEW QUESTION 16
          Note: This question is part of a series of questions that present the same Scenario. Each question I the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution while others might not have correct solution.
          You are implementing a batch processing solution by using Azure HDlnsight. You have a 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?

          • A. Use a shuffle join in an Apache Hive query that stores the data in a JSON format.
          • B. Use a broadcast join in an Apache Hive query that stores the data in an ORC format.
          • C. Increase the number of spark.executor.cores in an Apache Spark job that stores the data in a text format.
          • D. Increase the number of spark.executor.instances in an Apache Spark job that stores the data in a text format.
          • E. Decrease the level of parallelism in an Apache Spark job that Mores the data in a text format.
          • F. Use an action in an Apache Oozie workflow that stores the data in a text format.
          • G. Use an Azure Data Factory linked service that stores the data in Azure Data lake.
          • H. Use an Azure Data Factory linked service that stores the data In an Azure DocumentDB database.

          Answer: G

          Explanation: References: https://docs.microsoft.com/en-us/azure/data-factory/concepts-datasets-linkedservices

          NEW QUESTION 17
          You have an Apache Spark cluster in Azure HDInsight. You plan to join a large table and a lookup table.
          You need to minimize data transfers during the join operation. What should you do?

          • A. Use the reduceByKey function
          • B. Use a Broadcast variable.
          • C. Repartition the data.
          • D. Use the DISK_ONLY storage level.

          Answer: B

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