Associate-Cloud-Engineer Practice Questions

A.  

Deploy your solution on multiple standalone Compute Engine instances, and increase the number of existing instances wnen CPU utilization on Cloud Monitoring reaches a certain threshold.

B.  

Deploy your solution on multiple standalone Compute Engine instances, and replace existing instances with high-CPUinstances when CPU utilization on Cloud Monitoring reaches a certain threshold.

C.  

Deploy your solution to an instance group, and increase the number of available instances whenever you see high CPU utilization in Cloud Monitoring.

D.  

Deploy your solution to an instance group, and set the autoscaling based on CPU utilization.

A.  

Create a custom role, and add all the required compute.disks.list and compute, images.list permissions as includedPermissions. Grant the custom role to the user at the project level.

B.  

Create a custom role based on the Compute Image User role Add the compute.disks, list to theincludedPermissions field Grant the custom role to the user at the project level

C.  

Grant the Compute Storage Admin role at the project level.

D.  

Create a custom role based on the Compute Storage Admin role. Exclude unnecessary permissions from the custom role. Grant the custom role to the user at the project level.

A.  

Expose the application by using an internal passthrough Network Load Balancer.

B.  

Expose the application by using an external passthrough Network Load Balancer.

C.  

Expose the application by using a global external proxy Network Load Balancer.

D.  

Expose the application by using a regional external proxy Network Load Balancer.

A.  

Use Cloud Logging filters to create log-based metrics for firewall and instance actions. Monitor the changes and set up reasonable alerts.

B.  

Install Kibana on a compute Instance. Create a log sink to forward Cloud Audit Logs filtered for firewalls andcompute instances to Pub/Sub. Target the Pub/Sub topic to push messages to the Kibana instance. Analyze the logs on Kibana in real time.

C.  

Turn on Google Cloud firewall rules logging, and set up alerts for any insert, update, or delete events.

D.  

Create a log sink to forward Cloud Audit Logs filtered for firewalls and compute instances to Cloud Storage.Use BigQuery to periodically analyze log events in the storage bucket.

A.  

Filter the Activity log to view the Configuration category. Filter the Resource type to Service Account.

B.  

Filter the Activity log to view the Configuration category. Filter the Resource type to Google Project.

C.  

Filter the Activity log to view the Data Access category. Filter the Resource type to Service Account.

D.  

Filter the Activity log to view the Data Access category. Filter the Resource type to Google Project.

A.  

Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 275 days (365 – 90)

B.  

Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 365 days.

C.  

Use gsutil rewrite and set the Delete action to 275 days (365-90).

D.  

Use gsutil rewrite and set the Delete action to 365 days.

A.  

Export your bill to a Cloud Storage bucket, and then import into Cloud Bigtable for analysis.

B.  

Export your bill to a Cloud Storage bucket, and then import into Google Sheets for analysis.

C.  

Export your transactions to a local file, and perform analysis with a desktop tool.

D.  

Export your bill to a BigQuery dataset, and then write time window-based SQL queries for analysis.

A.  

Download the private key from the service account, and add it to each VMs custom metadata.

B.  

Download the private key from the service account, and add the private key to each VM’s SSH keys.

C.  

Grant the service account the IAM Role of Compute Storage Admin in the project called proj-vm.

D.  

When creating the VMs, set the service account’s API scope for Compute Engine to read/write.

A.  

Create a dataflow job that copies data from Cloud Bigtable and Cloud Storage for specific users.

B.  

Create a dataflow job that copies data from Cloud Bigtable and Cloud Spanner for specific users.

C.  

Create a Cloud Dataproc cluster that runs a Spark job to extract data from Cloud Bigtable and Cloud Storage for specific users.

D.  

Create two separate BigQuery external tables on Cloud Storage and Cloud Bigtable. Use the BigQuery console to join these tables through user fields, and apply appropriate filters.

A.  

Upload the code to Cloud Functions. Use Cloud Scheduler to start the application.

B.  

Create a container for the set of binaries. Use Cloud Scheduler to start a Cloud Run job for the container.

C.  

Create a container for the set of binaries Deploy the container to Google Kubernetes Engine (GKE) and use the Kubernetes scheduler to start the application.

D.  

Lift and shift to a VM on Compute Engine. Use an instance schedule to start and stop the instance.