Microsoft 70-775 Exam Questions and Answers 2021

NEW QUESTION 1
DRAG DROP
You have a domain-joined Azure HDInsight cluster. You plan to assign permissions to several support staff.
You need to assign roles to the staff so that they can perform specific tasks.
The solution must use the principle of least privilege.
Which role should you assign for each task? To answer, drag the appropriate roles to the correct tasks. Each role 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.

Answer:

Explanation:

NEW QUESTION 2
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: C

Explanation: References: https://blog.cloudera.com/blog/2015/03/how-to-tune-your-apache-spark-jobspart-2/

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.
You are implementing a batch processing solution by using Azure HDlnsight.
You need to integrate Apache Sqoop data and to chain complex jobs. The data and jobs will implement MapReduce. 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: F

NEW QUESTION 4
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 5
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:

Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-connect-excel-hiveodbc-driver

NEW QUESTION 6
Note: This question is part of a series of questions that present the same scenario. Each question in 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 a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
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 a consumer and a broker. Create a file import process to send messages.
Run the producer.
Does this meet the goal?

• A. Yes
• B. No

Answer: B

NEW QUESTION 7
You have an Apache Hadoop cluster in Azure HDInsiqht that has a head node and three data nodes. You have a MapReduce job.
You receive a notification that a data node failed.
You need to identity which component caused the failure. Which tool should you use?

• A. Job Tracker
• B. TaskTracker
• C. ResourceManager
• D. ApplicationMaster

Answer: C

NEW QUESTION 8
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 9
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 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.
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 two new brokers. Create a file import process to send messages. Run the producer.
Does this meet the goal?

• A. Yes
• B. No

Answer: B

NEW QUESTION 11
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 are designing the real-time portion of the input stream processing. The input will be a continuous stream of data and each record will be processed one at a time. The data will come from an Apache Kafka producer.
You need to identify which HDInsight cluster to use for the final processing of the input data. This will be used to generate continuous statistics and real-time analytics. The latency to process each record must be less than one millisecond and tasks must be performed in parallel.
Which type of cluster should you identify?

• A. Apache Storm
• B. Apache Hadoop
• C. Apache HBase
• D. Apache Spark

Answer: A

Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-storm-overview

NEW QUESTION 12
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 a NoSQL database to an HDInsight cluster. You will manage the servers that host the database by using Remote Desktop. The database must use the key/value pair format in a columnar model.
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: G

Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hbase-overview

NEW QUESTION 13
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

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:

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 support analysts can develop embedded analytics applications by using the least amount of development effort.
Which technology should you implement?

• A. Zeppelin
• B. Jupyter
• C. Apache Ambari
• D. Livy

Answer: D

Explanation: References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-apache-spark-livyrest-interface

NEW QUESTION 15
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.

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 16
You plan to copy data from Azure Blob storage to an Azure SQL database by using Azure Data factory. Which file formats can you use?

• A. binary, JSON, Apache Parquet, and ORC
• B. OXPS, binary, text and JSON
• C. XML, Apache Avro, text, and ORC
• D. text, JSON, Apache Avro, and Apache Parquet

Answer: D

Explanation: References: https://docs.microsoft.com/en-us/azure/data-factory/supported-file-formatsand-compression-codecs

NEW QUESTION 17
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 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 18
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 DocumentDBdatabase.

Answer: B