To improve the performance of visualization queries while minimizing maintenance overhead, using materialized views is the most effective solution. Here's why option C is the best choice:
Materialized Views:
Materialized views store the results of a query physically, allowing for faster access compared to regular views which execute the query each time it is accessed.
They can be automatically refreshed to reflect changes in the underlying data.
Incremental Updates:
The incremental updates capability of BigQuery materialized views ensures that only the changed data is processed during refresh operations, significantly improving performance and reducing computation costs.
This feature helps maintain up-to-date data in the materialized view with minimal processing time, which is crucial for data that needs to be no less than 4 hours old.
Performance and Maintenance:
By using materialized views, you can pre-compute and store the results of complex queries involving outer joins and analytic functions, resulting in faster query performance for data visualizations.
This approach also reduces the maintenance overhead, as BigQuery handles the incremental updates and refreshes automatically.
Steps to Implement:
Create Materialized Views:
Define materialized views for the visualization queries with the necessary configurations CREATE MATERIALIZED VIEW project.dataset.view_name AS SELECT ...
FROM ...
WHERE ...
Enable Incremental Updates:
Ensure that the materialized views are set up to handle incremental updates automatically.
Reference:
Update the data visualization tool to reference the materialized views instead of running the original queries directly.
BigQuery Materialized Views
Optimizing Query Performance

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.

To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here's why this is the best choice:
Retention Policy:
A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified.
This ensures data immutability.
Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.
Locking the Retention Policy:
Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.
Steps to Implement:
Set the Retention Policy:
Define a retention period for the bucket to ensure that all objects are protected for the required duration.
Lock the Retention Policy:
Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.
Reference Links:
Cloud Storage Retention Policy Documentation
How to Set a Retention Policy