Dataproc Serverless is the ideal choice for running Apache Spark jobs in Google Cloud with minimal changes and effort. It is designed to:
1. Minimize management effort: Dataproc Serverless removes the need to provision or manage clusters, as it dynamically allocates resources based on the job's requirements.
2. Compatibility: It supports Apache Spark 3, allowing you to run your existing batch job with little to no modification.
3. Resource customization: You can specify resource configurations (e.g., number of vCPUs and memory for executors) that align with your job's requirements (e.g., 8 vCPU and 16 GB memory).
4. Cloud-native integration: It natively integrates with Cloud Storage for input data and BigQuery for output, simplifying the migration process.

An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference:
https://cloud.google.com/bigquery/docs/views#authorized-views

C, E: By not acknowleding the pulled message, this result in it be putted back in Cloud Pub/Sub, meaning the messages accumulate instead of being consumed and removed from Pub/Sub. The same thing can happen ig the subscriber maintains the lease on the message it receives in case of an error. This reduces the overall rate of processing because messages get stuck on the first subscriber. Also, errors in Cloud Function do not show up in Stackdriver Log Viewer if they are not correctly handled.
A: No problem with publisher rate as the observed result is a higher number of messages and not a lower number.
B: if messages exceed the 10MB maximum, they cannot be published.
D: Cloud Functions automatically scales so they should be able to keep up.

https://cloud.google.com/architecture/managing-infrastructure-as-code