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 plan to create a new HBase table to collect the operations data. The workload will be distributed across regional servers. Apache Phoenix will be used on top of HBase for the operations data.
You need to ensure that write operations are distributed across the regional servers.
Which setting should you configure when you create the table?