Reference: https://cloud.google.com/kubernetes-engine/sandbox/
GKE Sandbox provides an extra layer of security to prevent untrusted code from affecting the host kernel on your cluster nodes when containers in the Pod execute unknown or untrusted code. Multi-tenant clusters and clusters whose containers run untrusted workloads are more exposed to security vulnerabilities than other clusters. Examples include SaaS providers, web-hosting providers, or other organizations that allow their users to upload and run code. When you enable GKE Sandbox on a node pool, a sandbox is created for each Pod running on a node in that node pool. In addition, nodes running sandboxed Pods are prevented from accessing other Google Cloud services or cluster metadata. Each sandbox uses its own userspace kernel. With this in mind, you can make decisions about how to group your containers into Pods, based on the level of isolation you require and the characteristics of your applications.
Ref: https://cloud.google.com/kubernetes-engine/docs/concepts/sandbox-pods

Comprehensive and Detailed In Depth Explanation:
The core requirements are secure access to a specific BigQuery dataset from a Compute Engine instance, using short-lived credentials, adhering to Google's best practices, and minimizing operational overhead.
A: Project-level IAM role: Granting the BigQuery Data Viewer role at the project level gives the service account broad access to all BigQuery datasets within that project. This violates the principle of least privilege, a fundamental security best practice, as the application should only have access to the designated dataset.
B: Hourly new service account with dataset-level role: While this aims to achieve short-lived credentials, the operational burden of creating, attaching, and managing IAM policies for a new service account every hour is significant and not a Google-recommended practice for routine access. It introduces unnecessary complexity and potential for errors.
C: Custom service account with dataset-level IAM role: This is the recommended and most efficient approach. You create a dedicated Google Cloud service account specifically for this application. You then grant this service account the necessary IAM role (e.g., BigQuery Data Viewer, or a more specific custom role) directly on the target BigQuery dataset. When the Compute Engine instance runs as this service account, the Google Cloud client libraries automatically handle the acquisition and rotation of short-lived OAuth 2.0 access tokens from the instance's metadata server. This eliminates the need to manage long-lived credentials (like service account keys) and ensures the application only has access to the intended dataset. This adheres to the principle of least privilege and minimizes operational costs.
D: Hourly new service account with project-level role: This option combines the high operational overhead of frequently creating new service accounts with the security risk of granting overly permissive project-level access. It is not a recommended practice.
Therefore, the most secure, cost-effective, and operationally efficient solution is to create a custom service account, attach it to the Compute Engine instance, and grant it the appropriate BigQuery IAM role specifically on the target dataset. The platform handles the short-lived credentials automatically.
Google Cloud Documentation References:
Creating and enabling service accounts for instances: https://cloud.google.com/compute/docs/access/create- enable-service-accounts-for-instances - Explains how to create and associate service accounts with Compute Engine VMs.
Granting, changing, and revoking access to resources: https://cloud.google.com/iam/docs/granting-changing- revoking-access - Details how to manage IAM policies and grant roles to service accounts on specific resources (like BigQuery datasets).
BigQuery IAM roles: https://cloud.google.com/bigquery/docs/control-access - Provides information on the various IAM roles available for controlling access to BigQuery resources at different levels (project, dataset, table, etc.).
Best practices for using service accounts: https://cloud.google.com/iam/docs/best-practices-for-using-service- accounts - Emphasizes the importance of the principle of least privilege and avoiding the management of long- lived service account keys when possible (relying on the metadata server for short-lived tokens).

Autopilot is more reliable and stable release gives more time to fix issues in new version of GKE

/25:
CIDR to IP Range
Result
CIDR Range 172.16.20.128/25
Netmask 255.255.255.128
Wildcard Bits 0.0.0.127
First IP 172.16.20.128
First IP (Decimal) 2886734976
Last IP 172.16.20.255
Last IP (Decimal) 2886735103
Total Host 128
CIDR
172.16.20.128/25
/24:
CIDR to IP Range
Result
CIDR Range 172.16.20.128/24
Netmask 255.255.255.0
Wildcard Bits 0.0.0.255
First IP 172.16.20.0
First IP (Decimal) 2886734848
Last IP 172.16.20.255
Last IP (Decimal) 2886735103
Total Host 256
CIDR
172.16.20.128/24

Specifying conditions for alerting policies This page describes how to specify conditions for alerting policies.
The conditions for an alerting policy define what is monitored and when to trigger an alert. For example, suppose you want to define an alerting policy that emails you if the CPU utilization of a Compute Engine VM instance is above 80% for more than 3 minutes. You use the conditions dialog to specify that you want to monitor the CPU utilization of a Compute Engine VM instance, and that you want an alerting policy to trigger when that utilization is above 80% for 3 minutes. https://cloud.google.com/monitoring/alerts/ui-conditions-ga
https://cloud.google.com/monitoring/alerts/using-alerting-ui https://cloud.google.com/monitoring/support
/notification-options