Online Access Free 070-775 Exam Questions

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 server
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.shuffle.partitions
• C.spark.sql.files.openCostInBytes
• D.spark.sql.broadcastTimeout

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 server
The business analysts require queries to monitor the sales dat
a. 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.

Correct Answer:

DRAG DROP
You are evaluating the use of Azure HDInsight clusters for various workloads.
Which type of HDInsight cluster should you create for each workload? To answer, drag the appropriate cluster types to the correct workloads. Each cluster type 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.

Correct Answer:

References: https://www.blue-granite.com/blog/how-to-choose-the-right-hdinsight-cluster

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 server
You need to ensure that the data analysts can use the notebooks.
What should you install?

• A.Zeppelin in the batch layer
• B.Jupyter in the batch layer
• C.Zeppelin on workstations that access the batch layer
• D.Jupyter on workstations that access the batch layer

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 targets.
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.

Correct Answer: