Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.
Log entries are held in Stackdriver Logging for a limited time known as the retention period which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.
Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.
While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.
Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks
Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.
Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.
You can export logs by creating one or more sinks that include a logs query and an export destination.
Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.
Ref: https://cloud.google.com/logging/docs/export/configure_export_v2
Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.
Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks
Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.
Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.
For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.
Ref: https://cloud.google.com/bigquery/docs/best-practices-storage
Reference: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit- logging

The best option is to attach a single service account to the compute instances and add minimal rights to the service account. Then, allow the service account to impersonate a Cloud Identity user with elevated permissions to create, update, or delete resources. This way, the service account can use short-lived access tokens to authenticate to Google Cloud APIs without needing to manage service account keys. This option follows the principle of least privilege and reduces the risk of credential leakage and misuse.
Option A is not recommended because it requires human intervention, which can slow down the CI/CD pipeline and introduce human errors. Option C is not secure because it grants all required IAM permissions to a single service account, which can increase the impact of a compromised key. Option D is not cost-effective because it requires creating and managing multiple service accounts and keys, as well as using a secret manager service.
1: https://cloud.google.com/iam/docs/impersonating-service-accounts
2: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys
3: https://cloud.google.com/iam/docs/understanding-service-accounts

https://cloud.google.com/resource-manager/docs/project-migration#oauth_consent_screen
https://cloud.google.com/resource-manager/docs/project-migration

Reference:https://cloud.google.com/solutions/federating-gcp-with-active-directory-introduction Directory Sync Google Cloud Directory Sync enables administrators to synchronize users, groups and other data from an Active Directory/LDAP service to their Google Cloud domain directoryhttps://tools.google.com
/dlpage/dirsync/

This command correctly deletes the deployment. Pods are managed by kubernetes workloads (deployments).
When a pod is deleted, the deployment detects the pod is unavailable and brings up another pod to maintain the replica count. The only way to delete the workload is by deleting the deployment itself using the kubectl delete deployment command.
* $ kubectldeletedeployment nginx
* deployment.apps nginx deleted
Ref: https://kubernetes.io/docs/reference/kubectl/cheatsheet/#deleting-resources