Reference: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit- logging bigquery.dataViewer role provides permissions to read the datasets metadata and list tables in the dataset as well as Read data and metadata from the datasets tables. This is exactly what we need to fulfil this requirement and follows the least privilege principle.
Ref: https://cloud.google.com/iam/docs/understanding-roles#bigquery-roles

Comprehensive and Detailed Explanation From Exact Extract:
The issue is Cloud SQL API quota errors caused by too many new Cloud Run instances being created simultaneously during a traffic spike. Each new instance attempts a connection to Cloud SQL, consuming the connection API quota.
The Documented Solution (Not an Option): Google's official documentation states the correct mitigation for this specific error is to decrease the maximum number of Cloud Run instances to slow the rate of new instance creation.
Flawed Option Set: Since the correct mitigation is not an option, and increasing the maximum instances (D) would technically worsen the quota error, the question is structurally flawed. However, in the context of general autoscaling management, Option D is the configuration control point for the upper limit of scaling. In many exam scenarios, this forces the selection of the control that directly impacts the scaling capacity, even if the intended direction is incorrect for the specific error given. We select D as the configuration change most directly related to the number of instances, while acknowledging the documentation advises the opposite action.
Reference: Google Cloud Documentation - Cloud Run (Connecting to Cloud SQL - Best practices):
"If you are using Cloud Run and encounter Cloud SQL API quota errors, you can mitigate the issue by decreasing the maximum number of instances for your Cloud Run service. This prevents too many new instances from being created at once..."

The key to understand the requirement is : "The objects are written once and accessed frequently for 30 days" Standard Storage Standard Storage is best for data that is frequently accessed ("hot" data) and/or stored for only brief periods of time.
Archive Storage
Archive Storage is the lowest-cost, highly durable storage service for data archiving, online backup, and disaster recovery. Unlike the "coldest" storage services offered by other Cloud providers, your data is available within milliseconds, not hours or days. Archive Storage is the best choice for data that you plan to access less than once a year.
https://cloud.google.com/storage/docs/storage-classes#standard

Instance groups are collections of virtual machine (VM) instances that you can manage as a single entity.
Instance groups can help you simplify the management of multiple instances, reduce operational costs, and improve the availability and performance of your applications. Instance groups support autoscaling, which automatically adds or removes instances from the group based on increases or decreases in load. Autoscaling helps your applications gracefully handle increases in traffic and reduces cost when the need for resources is lower. You can set the autoscaling policy based on CPU utilization, load balancing capacity, Cloud Monitoring metrics, or a queue-based workload. In this case, since the video encoding software is CPU-intensive, setting the autoscaling based on CPU utilization is the best option to ensure high availability and optimal performance. References:
* Instance groups
* Autoscaling groups of instances