| Exam Code/Number: | 70-775Join the discussion |
| Exam Name: | Perform Data Engineering on Microsoft Azure HDInsight |
| Certification: | Microsoft |
| Question Number: | 63 |
| Publish Date: | Sep 02, 2026 |
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Rating
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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 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?
You have an 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?
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 HDInsight cluster that will provide in-memory processing, interactive queries, and micro-batch stream processing. The cluster has the following requirements:
Uses Azure Data Lake Store as the primary storage
Can be used by HDInsight applications
What should you do?
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?
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?
