Professional-Cloud-Architect Exam Dumps - Google Certified Professional - Cloud Architect (GCP)

App Engine

GKE On-Prem

Compute Engine

Google Kubernetes Engine

Port the application code to run on Google App Engine.

Integrate Cloud Dataflow into the application to capture real-time metrics.

Instrument the application with a monitoring tool like Stackdriver Debugger.

Select an automation framework to reliably provision the cloud infrastructure.

Deploy a continuous integration tool with automated testing in a staging environment.

Migrate from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable.

Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to loadbalance the HTTPS traffic.

Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a

Service resource of type LoadBalancer to load-balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.

1. Set a CORS configuration in the target Cloud Storage bucket where the base URL of the App

Engine application is an allowed origin.

2. Use the Cloud Storage Signed URL feature to generate a POST URL.

1. Set a CORS configuration in the target Cloud Storage bucket where the base URL of the App

Engine application is an allowed origin.

2. Assign the Cloud Storage WRITER role to users who upload files.

1. Use the Cloud Storage Signed URL feature to generate a POST URL.

2. Use App Engine default credentials to sign requests against Cloud Storage.

1. Assign the Cloud Storage WRITER role to users who upload files.

2. Use App Engine default credentials to sign requests against Cloud Storage.

Create a group per country. Add analysts to their respective country-groups. Create a single group

‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of

BigQuery jobUser. Share the appropriate dataset with view access with each respective analyst country-group.

Create a group per country. Add analysts to their respective country-groups. Create a single group

‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of

BigQuery jobUser. Share the appropriate tables with view access with each respective analyst countrygroup.

Create a group per country. Add analysts to their respective country-groups. Create a single group

‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of

BigQuery dataViewer. Share the appropriate dataset with view access with each respective analyst

country-group.

Create a group per country. Add analysts to their respective country-groups. Create a single group

‘all_analysts’, and add all country-groups as members. Grant the ‘all-analysis’ group the IAM role of

BigQuery dataViewer. Share the appropriate table with view access with each respective analyst countrygroup.

Redesign the application to be cloud-native using microservices before migration to eliminate dependencies and simplify workload testing.

Plan a " lift and shift " migration for all application tiers to be executed simultaneously over a single weekend to minimize downtime.

Use dependency mapping to group application components into waves, create a phased migration plan, and incorporate performance testing for each wave.

Prioritize the establishment of a high-bandwidth, low-latency Cloud Interconnect connection, and then plan to migrate the database servers first.

Web applications deployed using App Engine standard environment

RabbitMQ deployed using an unmanaged instance group

Hadoop/Spark deployed using Cloud Dataproc Regional in High Availability mode

Jenkins, monitoring, bastion hosts, security scanners services deployed on custom machine types