Professional-Cloud-Architect 無料問題集「Google Certified Professional - Cloud Architect (GCP)」

（A）Ensure every code check-in is peer reviewed by a security SME.

（B）Ensure you have stubs to unit test all interfaces between components.

（C）Enable code signing and a trusted binary repository integrated with your CI/CD pipeline.

（D）Run a vulnerability security scanner as part of your continuous-integration /continuous-delivery (CI/CD) pipeline.

（E）Use source code security analyzers as part of the CI/CD pipeline.

（A）1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.

（B）1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team
3 Assign access controls to the dataset that contains the view

（C）1 Create a dataset for the data science team
2 Create views of tables that you want to share excluding Pll
3 Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset

（D）1. Create a dataset for the data science team.
2. Create materialized views of tables that you want to share, excluding Pll
3. Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset

（A）The effective policy is the intersection of the policy set at the node and policies inherited from its ancestors

（B）The effective policy is determined only by the policy set at the node

（C）The effective policy is the union of the policy set at the node and policies inherited from its ancestors

（D）The effective policy is the policy set at the node and restricted by the policies of its ancestors

（A）Store all state into local SSD, snapshot the persistent disks, and terminate the VM.

（B）Use the --no-auto-delete flag on all persistent disks and stop the VM.

（C）Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM.

（D）Apply VM CPU utilization label and include it in the BigQuery billing export.

（E）Use the -auto-delete flag on all persistent disks and terminate the VM.

（F）Use Google BigQuery billing export and labels to associate cost to groups.

（A）Configure a Kubernetes autoscaling based on the subscription/push_request metric.

（B）Use the -enable- autoscaling flag when you create the Kubernetes cluster

（C）Configure a Kubernetes autoscaling based on the subscription/num_undelivered message metric.

（D）Use kubectl autoscale deployment APP_NAME -max 6 -min 2 -cpu- percent 50 to configure Kubernetes autoscaling deployment

（A）1. Attach a regional SSD persistent Ask to the first instance
2. In case of a zone outage, force-attach the disk to the other instance

（B）1 Create a Cloud Storage bucket
2. Mount the bucket into the first instance with gcs-fuse
3. In case of a zone outage, mount the Cloud Storage bucket to the second instance with gcs-fuse

（C）1. Attach a persistent SSD disk to the first instance
2. Create a snapshot every hour
3. In case of a zone outage, recreate a persistent SSD disk in the second instance where data is coming from the created snapshot

（D）1 Attach a local SSD lo the first instance disk
2. Execute an rsync command every hour where the target is a persistent SSD disk attached to the second instance
3. In case of a zone outage, use the second instance

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

（B）RabbitMQ deployed using an unmanaged instance group

（C）Web applications deployed using App Engine standard environment

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

（A）Develop the application for App Engine Flexible environment using a custom runtime

（B）Develop the application for App Engine standard environment

（C）Use a Managed Instance Group when deploying to Compute Engine

（D）Develop an application with containers, and deploy to Google Kubernetes Engine (GKE)

（A）Configure HashiCorp Vault on Compute Engine, and use customer managed encryption keys and Cloud Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to be used by the application platform.

（B）Use Kubernetes Secrets, which are obfuscated by default. Configure these Secrets to be used by the application platform.

（C）Configure Workload Identity and service accounts to be used by the application platform.

（D）Configure Kubernetes Secrets to store the secret, enable Application-Layer Secrets Encryption, and use Cloud Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to be used by the application platform.

（A）Google Cloud Storage

（B）Google Cloud Bigtable

（C）Google Cloud SQL

（D）Google BigQuery

（A）Use the Service Mesh visualization in the Cloud Console to inspect the telemetry between the microservices.

（B）Use Anthos Config Management to create a namespaceSelector selecting the relevant cluster namespace. On the Google Cloud Console page for Google Kubernetes Engine, visit the workloads and filter on the namespace. Inspect the configurations of the filtered workloads.

（C）Use Anthos Config Management to create a ClusterSelector selecting the relevant cluster. On the Google Cloud Console page for Google Kubernetes Engine, view the Workloads and filter on the cluster. Inspect the configurations of the filtered workloads.

（D）Reinstall istio using the default istio profile in order to collect request latency. Evaluate the telemetry between the microservices in the Cloud Console.

（A）Set up a filter in Cloud Logging and a topic in Pub/Sub to publish the logs

（B）Set up a filter in Cloud Logging and a Cloud Storage bucket as an export target for the logs you want to save

（C）Enable the Compute Engine API and then enable logging on the firewall rules that match the traffic you want to save

（D）Set up a Cloud Logging Dashboard titled Cloud VPN Logs, and then add a chart that queries for the VPN metrics over a one-year time period

（A）Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.

（B）Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.

（C）Deploy the update in a new VPC, and use Google's global HTTP load balancing to split traffic between the update and current applications.

（D）Deploy the update as a new App Engine application, and use Google's global HTTP load balancing to split traffic between the new and current applications.

（A）Configure a Google Cloud Dedicated Interconnect.

（B）Configure their replication to use UDP.

（C）Restore their database daily using Google Cloud SQL.

（D）Send the replicated transaction to Google Cloud Pub/Sub.

（E）Add additional VPN connections and load balance them.

（A）Compute Engine

（B）Google Kubernetes Engine

（C）GKE On-Prem

（D）App Engine

（A）Use an IP range on Google Cloud that does not overlap with the range you use on-premises for your primary IP range and use a secondary range with the same IP range as you use on-premises

（B）Use the same IP range on Google Cloud as you use on-premises

（C）Use the same IP range on Google Cloud as you use on-premises for your primary IP range and use a secondary range that does not overlap with the range you use on-premises

（D）Use an IP range on Google Cloud that does not overlap with the range you use on-premises