A bank is using Amazon Managed Streaming for Apache Kafka (Amazon MSK) to populate real-time data into a data lake. The data lake is built on Amazon S3, and data must be accessible from the data lake within 24 hours. Different microservices produce messages to different topics in the cluster. The cluster is created with 8 TB of Amazon Elastic Block Store (Amazon EBS) storage and a retention period of 7 days.
The customer transaction volume has tripled recently, and disk monitoring has provided an alert that the cluster is almost out of storage capacity.
What should a data analytics specialist do to prevent the cluster from running out of disk space?
A. Use the Amazon MSK console to triple the broker storage and restart the cluster.
B. Create an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric. Automatically flush the oldest messages when the value of this metric exceeds 85%.
C. Create a custom Amazon MSK configuration. Set the log.retention.hours parameter to 48. Update the cluster with the new configuration file.
D. Triple the number of consumers to ensure that data is consumed as soon as it is added to a topic.
The customer transaction volume has tripled recently, and disk monitoring has provided an alert that the cluster is almost out of storage capacity.
What should a data analytics specialist do to prevent the cluster from running out of disk space?
A. Use the Amazon MSK console to triple the broker storage and restart the cluster.
B. Create an Amazon CloudWatch alarm that monitors the KafkaDataLogsDiskUsed metric. Automatically flush the oldest messages when the value of this metric exceeds 85%.
C. Create a custom Amazon MSK configuration. Set the log.retention.hours parameter to 48. Update the cluster with the new configuration file.
D. Triple the number of consumers to ensure that data is consumed as soon as it is added to a topic.