GCP Professional Data Engineer Practice Question

A media-streaming company runs an Apache Beam pipeline on Cloud Dataflow in the us-central1 region. The job keeps several terabytes of user session data in Redis to perform low-latency joins. Management wants the pipeline to survive a complete zonal outage without manual intervention while keeping operational overhead and complexity to a minimum. Which approach best meets these requirements?

Run open-source Redis Cluster on a stateful GKE deployment distributed across three zones and manage failover with custom scripts and Kubernetes operators.
Create a Memorystore for Redis Cluster instance in us-central1. Configure the Dataflow pipeline to connect through the cluster's discovery endpoint and rely on its built-in multi-zone shard replication.
Provision two Basic Tier Memorystore for Redis instances, one in us-central1-a and one in us-central1-b, and modify the Dataflow job to write to both instances for redundancy.
Deploy a Standard Tier Memorystore for Redis instance in us-central1-a and create a Cloud SQL read replica in a different zone to take over if the primary zone fails.

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Answer Description

Memorystore for Redis Cluster is a regional service that automatically shards data and places each shard's primary and replica in separate zones inside the region. If an entire zone fails, clients reconnect to the surviving replicas without manual reconfiguration, so the Dataflow job continues with minimal disruption. Running your own Redis on GKE or managing dual basic instances would require you to build and operate the sharding, replication, and failover logic yourself. A single-zone Standard Tier instance, even with a Cloud SQL read replica, would not protect against a zonal outage and mixes unrelated services. Therefore, a regional Memorystore for Redis Cluster instance with its built-in multi-zone placement and automatic failover delivers the desired high availability with the least operational effort.

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