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Question 8

For this question, refer to the HipLocal case study.

How should HipLocal redesign their architecture to ensure that the application scales to support a large increase in users?

Options:

Use Google Kubernetes Engine (GKE) to run the application as a microservice. Run the MySQL database on a dedicated GKE node.

Use multiple Compute Engine instances to run MySQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use Memorystore to store session information and CloudSQL to store state information. Use a Google Cloud-managed load balancer to distribute the load between instances. Use managed instance groups for scaling.

Use a Cloud Storage bucket to serve the application as a static website, and use another Cloud Storage bucket to store user state information.

Question 9

For this question refer to the HipLocal case study.

HipLocal wants to reduce the latency of their services for users in global locations. They have created read replicas of their database in locations where their users reside and configured their service to read traffic using those replicas. How should they further reduce latency for all database interactions with the least amount of effort?

Options:

Migrate the database to Bigtable and use it to serve all global user traffic.

Migrate the database to Cloud Spanner and use it to serve all global user traffic.

Migrate the database to Firestore in Datastore mode and use it to serve all global user traffic.

Migrate the services to Google Kubernetes Engine and use a load balancer service to better scale the application.

Question 10

HipLocal has connected their Hadoop infrastructure to GCP using Cloud Interconnect in order to query data stored on persistent disks.

Which IP strategy should they use?

Options:

Create manual subnets.

Create an auto mode subnet.

Create multiple peered VPCs.

Provision a single instance for NAT.

Question 11

For this question, refer to the HipLocal case study.

How should HipLocal increase their API development speed while continuing to provide the QA team with a stable testing environment that meets feature requirements?