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It was more of a real time scenario based questions than of theoretical. As I have attended for DevOps questions are mostly above the helm charts, Kubernetes , and micro service deployments and scaling as well , Also touch based on how well I have understood my existing infrastructure
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Staff Software Engineer - DevOps Role

Interviewed at Imperva

4.2★
May 29, 2023

It was more of a real time scenario based questions than of theoretical. As I have attended for DevOps questions are mostly above the helm charts, Kubernetes , and micro service deployments and scaling as well , Also touch based on how well I have understood my existing infrastructure

In a managed Apache Hadoop environment—like an Amazon EMR cluster—when the storage capacity on your cluster fills up, there is no convenient solution to deal with it. This situation occurs because you set up Amazon Elastic Block Store (Amazon EBS) volumes and configure mount points when the cluster is launched, so it’s difficult to modify the storage capacity after the cluster is running. The feasible solutions usually involve adding more nodes to your cluster, backing up your data to a data lake, and then launching a new cluster with a higher storage capacity. Or, if the data that occupies the storage is expendable, removing the excess data is usually the way to go.
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Staff Software Engineer

Interviewed at Fidelity National Financial

3.9★
Jun 22, 2023

In a managed Apache Hadoop environment—like an Amazon EMR cluster—when the storage capacity on your cluster fills up, there is no convenient solution to deal with it. This situation occurs because you set up Amazon Elastic Block Store (Amazon EBS) volumes and configure mount points when the cluster is launched, so it’s difficult to modify the storage capacity after the cluster is running. The feasible solutions usually involve adding more nodes to your cluster, backing up your data to a data lake, and then launching a new cluster with a higher storage capacity. Or, if the data that occupies the storage is expendable, removing the excess data is usually the way to go.

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