Google Professional-Cloud-Developer Exam With Confidence Using Practice Dumps

Select a virtual machine (VM) size with higher CPU for Cloud Build runs.

Deploy a Container Registry on a Compute Engine VM in a VPC, and use it to store the final images.

Cache the Docker image for subsequent builds using the -- cache-from argument in your build config file.

Change the base image in the Dockerfile to ubuntu:latest, and install Python 3.7 using a package manager utility.

Store application source code on Cloud Storage, and configure the pipeline to use gsutil to download the source code.

Include unit tests in their code, and prevent deployments to QA until all tests have a passing status.

Include performance tests in their code, and prevent deployments to QA until all tests have a passing status.

Create health checks for the QA environment, and redeploy the APIs at a later time if the environment is unhealthy.

Redeploy the APIs to App Engine using Traffic Splitting. Do not move QA traffic to the new versions if errors are found.

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.