70-775 Exam
All About 70-775 exam question Oct 2021
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Q21. 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: A
Q22. You have an Apache Spark cluster in Azure HDInsight. You execute the following command,
%spark
import org.aache.spark.sql.hive.orc._ import org.apcahe.spark.sql._
What is the result of running the command?
A. the Hive ORC library is imported to Spark and external tables in ORC format are created.
B. the Spark library is imported and the data is loaded to an Apache Hive table.
C. the Hive ORC library is imported to Spark arid the ORC-formatted data stored in Apache Hive tables becomes accessible
D. the Spark library is imported and Scala functions are executed
Answer: D
Q23. 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
Q24. 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:
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.
Answer:
Q25. 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 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?
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: B
Q26. HOTSPOT
You install the Microsoft Hive ODBC Driver on a computer that runs Windows 10 and has the 64-bit version of Microsoft Office 2021 installed.
You deploy a new Apache Interactive Hive cluster in Azure HDInsight. The cluster is hosted at myHDICluster.azurehdinsignt.net and contains a Hive table named hivesampletable that has 200,000 rows.
You plan to use HiveQL exclusively for the queries. The queries will return from 6,000 to 10,000 rows 90 percent of the time.
You need to configure a data source to ensure that you can use Microsoft Excel to access the data. The solution must ensure that the Hive queries execute as quickly as possible.
How should you configure the Advanced Options from the Microsoft Hive ODBC Driver DSN Setup dialog box? To answer select the appropriate options in the answer area.
NOTE:
Each correct selection is worth one point.
Answer:
Q27. 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 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 tanks for the job. 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: A