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

（A）Upload your files into Cloud Storage. Use Cloud Datalab to explore and clean your data.

（B）Upload your files into Cloud Storage. Use Cloud Dataprep to explore and clean your data.

（C）Connect Cloud Datalab to your on-premises systems. Use Cloud Datalab to explore and clean your data.

（D）Connect Cloud Dataprep to your on-premises systems. Use Cloud Dataprep to explore and clean your data.

（A）Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline store all non-PII data in BigQuery and store all Pll data in a Cloud Storage bucket that has a retention policy set.

（B）Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline use the Cloud Data Loss Prevention (Cloud DLP) API to remove any Pll data Store the result in BigQuery

（C）Ask the external partners to upload an data on Cloud Storage Configure Bucket Lock for the bucket Create a Dataflow pipeline to read the data from the bucket As part of the pipeline, use the Cloud Data Loss Prevention (Cloud DIP) API to remove any Pll data Store the result in BigQuery

（D）Ask the external partners to import ail data in your BigQuery dataset Create a dataflow pipeline to copy the data into a new table As part of the Dataflow bucket skip all data in columns that have Pll data

（A）Create a read replica instance in a different region

（B）Create a failover replica instance in the same region, but in a different zone

（C）Create a failover replica instance in a different region

（D）Create a read replica instance in the same region, but in a different zone

（A）Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.

（B）Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.

（C）Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.

（D）Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.

（A）Use a global HTTP(s) load balancer with Cloud CDN enabled

（B）Create a second GKE cluster in asia-southeast1, and expose both API's using a Service of type Load Balancer. Add the public Ips to the Cloud DNS zone

（C）Increase the memory and CPU allocated to the application in the cluster

（D）Create a second GKE cluster in asia-southeast1, and use kubemci to create a global HTTP(s) load balancer

（A）Use Puppet to create the managed instance group and install the OS package dependencies.

（B）Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.

（C）Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.

（D）Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.

（A）Configure a Cloud SQL instance with high availability enabled.

（B）Configure a Cloud Spanner instance with a regional instance configuration.

（C）Set up SQL Server Always On Availability Groups using Windows Failover Clustering. Place nodes in different zones.

（D）Set up SQL Server on Compute Engine, using Always On Availability Groups using Windows Failover Clustering. Place nodes in different subnets.

（A）Use Compute Engine to create an Active Directory (AD) domain controller that is a replica of the onpremises AD domain controller using Google Cloud Directory Sync.

（B）Use the Admin Directory API to authenticate against the Active Directory domain controller.

（C）Use Cloud Identity-Aware Proxy configured to use the on-premises Active Directory domain controller as an identity provider.

（D）Use Google Cloud Directory Sync to synchronize Active Directory usernames with cloud identities and configure SAML SSO.

（A）Cloud Run and Cloud Bigtable

（B）Cloud Run and BigQuery

（C）A Compute Engine autoscaling managed instance group and BigQuery

（D）A Compute Engine autoscaling managed instance group and Cloud Bigtable

（A）Configure their load balancer to pass through the User-Agent HTTP header when the value matches GoogleStackdriverMonitoring-UptimeChecks (https://cloud.google.com/monitoring)

（B）Configure their legacy web servers to allow requests that contain user-Agent HTTP header when the value matches GoogleStackdriverMonitoring- UptimeChecks (https://cloud.google.com/monitoring)

（C）In the Cloud Platform Console download the list of the uptime servers' IP addresses and create an inbound firewall rule

（D）Install the Stackdriver agent on all of the legacy web servers.

（A）1 Create folders under the Organization resource named "Development" and Production'
2 Grant all developers the Project Creator IAM role on the ""Development1 folder 3. Move the developer projects into the "Development" folder
4 Set the policies for all projects on the Organization
5 Additionally set the production policies on the "Production" folder

（B）1 Create a folder under the Organization resource named "Production '
2 Grant all developers the Project Creator IAM role on the Organization 3. Move the developer projects into the Organization
4 Set the policies for all projects on the Organization
5 Additionally set the production policies on the 'Production" folder

（C）1 Create a second Google Workspace account and Organization
2 Grant all developers the Project Creator IAM role on the new Organization
3 Move the developer projects into the new Organization
4 Set the policies for all projects on both Organizations.
5 Additionally set the production policies on the original Organization

（D）1 Designate the Organization for production projects only
2 Ensure that developers do not have the Project Creator IAM role on the Organization
3 Create development projects outside of the Organization using the developer Google Workspace accounts
4 Set the policies for all projects on the Organization
5 Additionally set the production policies on the individual production projects

（A）1. Create a Cloud Filestore NFS volume and attach it to the backend Compute Engine instances
2. Download popular songs in Cloud Filestore
3. Serve music Wes directly from the backend Compute Engine instance

（B）1. Create a managed instance group with Compute Engine instances
2. Create a global toad balancer and configure ii with two backbends
* Managed instance group
* Cloud Storage bucket
3. Enable Cloud CDN on the bucket backend

（C）1. Mount the Cloud Storage bucket using gcsfuse on all backend Compute Engine instances
2. Serve muse files directly from the backend Compute Engine instance

（D）1. Copy popular songs into CloudSQL as a blob
2. Update application code to retrieve data from CloudSQL when Cloud Storage is overloaded

（A）Create GKE CPU Utilization and Memory Utilization as service level indicators.

（B）Create Request Latency and Error Rate as service level indicators.

（C）Create Server Uptime and Error Rate as service level indicators.

（D）Create CPU Utilization and Request Latency as service level indicators.

（A）They should add canary tests so developers can measure how much of an impact the new release causes to latency

（B）They should add additional unit tests and production scale load tests on their cloud staging environment.

（C）They should enable google stack driver debugger on the application code to show errors in the code

（D）They should run the end to end tests in the cloud staging environment to determine if the code is working as intended.