Tips to Pass 70-775 Exam (11 to 20)

Q1. 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: H

Q2. 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: D

Q3. 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

Q4. 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 consumers. Create a file import process to send messages. Start the producer.

Does this meet the goal?

A. Yes

B. No

Answer: B

Q5. 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

Q6. 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

Q7. 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:

Q8. 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?