This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table.
Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:
Exporting table data | BigQuery | Google Cloud
Storage classes | Cloud Storage | Google Cloud
Retention policies and retention periods | Cloud Storage | Google Cloud Federated queries | BigQuery | Google Cloud

By assigning a function with notification logic to the on_failure_callback parameter, you can customize the action that is taken when a task fails in your DAG1. For example, you can send an email, a Slack message, or a PagerDuty alert to notify yourself or your team about the task failure2. This option is more flexible and reliable than configuring a Cloud Monitoring alert on the sla_missed metric, which only triggers when a task misses its scheduled deadline3. The sla_miss_callback parameter is also related to the sla_missed metric, and it is executed when the task instance has not succeeded and the time is past the task's scheduled execution date plus its sla4. The on_retry_callback parameter is executed before a task is retried4. These options are not suitable for notifying when a task does not succeed, as they depend on the task's schedule and retry settings, which may not reflect the actual task completion status. References:
* 1: Callbacks | Cloud Composer | Google Cloud
* 2: How to Send an Email on Task Failure in Airflow - Astronomer
* 3: Monitoring SLA misses | Cloud Composer | Google Cloud
* 4: BaseOperator | Apache Airflow Documentation

using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com
/bigtable/docs/schema-design]
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-
series#ensure_that_your_row_key_avoids_hotspotting

To securely connect multiple applications with dynamic public IP addresses to a Cloud SQL instance using public IP, the Cloud SQL Auth proxy is the best solution. This proxy provides secure, authorized connections to Cloud SQL instances without the need to configure authorized networks or deal with IP whitelisting complexities.
Cloud SQL Auth Proxy:
The Cloud SQL Auth proxy provides secure, encrypted connections to Cloud SQL.
It uses IAM permissions and SSL to authenticate and encrypt the connection, ensuring data security in transit.
By using the proxy, you avoid the need to constantly update authorized networks as the proxy handles dynamic IP addresses seamlessly.
Authorized Network Configuration:
Leaving the authorized network empty means no IP addresses are explicitly whitelisted, relying solely on the Auth proxy for secure connections.
This approach simplifies network management and enhances security by not exposing the Cloud SQL instance to public IP ranges.
Dynamic IP Handling:
Applications with dynamic IP addresses can securely connect through the proxy without the need to modify authorized networks.
The proxy authenticates connections using IAM, making it ideal for environments where application IPs change frequently.
Google Data Engineer References:
Using Cloud SQL Auth Proxy
Cloud SQL Security Overview
Setting up the Cloud SQL Auth Proxy
By using the Cloud SQL Auth proxy, you ensure secure, authorized connections for applications with dynamic public IPs without the need for complex network configurations.