Exam Professional Cloud Architect topic 1 question 128 discussion

Any correct answer must involve Cloud Bigtable over BigQuery since Bigtable is optimized for heavy write loads. That leaves B and D. I would suggest B b/c it is lower cost ("The business wants to keep costs low")

B is correct answer.

Cloud Run and Cloud Bigtable is the best choice because it meets all the requirements: Cloud Run can scale automatically to handle 500,000 requests per second and scales to zero during periods of no requests, reducing costs. Cloud Bigtable is designed for real-time queries with exact match attributes. Autoscaling Managed Instance Group can not scale to zero during periods of no requests*

It's hard for Cloud Run to scale to accept 500k rps so choosing Option D

Not sure why this is voted between B and D. It should be A. MIG wont support, that rules out C and D. between BQ and BT, please see that "data will be queried later in real time, based on exact matches of a known set of attributes". This is supported by BQ alone. So I would go with A.

B is correct

https://cloud.google.com/run/quotas There is no direct limit for: The size of container images you can deploy. The number of concurrent requests served by a Cloud Run service.

Cloud Run can handle this amount if there were like 500 instances which would cost a pretty ridiculous amount per minute, so unfortunately there isnt enough information in this question around how long the gaps are without data to make a proper decision. Autoscaling Managed Instance Groups can scale to zero and 500k per second would be relatively easily handled by a few instances.

50,000 rps At first I thought Cloud Run could not handle this request rate and then chose D. After a little bit of research on the docs I changed my mind to B. On each instance concurrency, it clearly says > By default each Cloud Run instance can receive up to 80 requests at the same time; you can increase this to a maximum of 1000 https://cloud.google.com/run/docs/about-concurrency The maximum number of auto-scaling instances by default is 100, which can be configured depending on the regional quota. With the default max instances it can already handle 100 * 1000 = 100,000 requests concurrently, which should be able to achieve the 50,000 rps requirement. https://cloud.google.com/run/docs/about-instance-autoscaling

Cloud Run can't support 50,000 requests per second. Correct answer should be D.

Cloud Run can't handle 50,000 requests per second A & B is eliminated

Not receive any request = Cloud Run

At first I through its B, but then I thought about the number of requests that will be over 1 minute, if we calculated it = 30 million request per minute, and based on cloud run pricing this will cost only for the number of requests: 24 USD. so cloud run will cost the company 24 USD / min. which might be a very costly option. But in the cloud run pricing there is 2 modes: - CPU allocated when receiving requests: and there is a cost for CPU and requests - CPU always allocated: and there is only a cost for the CPU and zero price for the number of requests. I think we need someone experiencing the billing of a cloud run under a heavy load like this :)

Apart from the reason that cloud run can scale to zero, another benefit in this scenario is the fact Cloud run will provide out of the box revision maintenance for the web service.

Compute: Cloud Run vs. Compute Engine autoscaling managed instance group Cloud Run wins because can scale down up to 0 instances -> in Spike workflows will be cheaper. Storage: BigQuery vs. Big Table. 500,000 requests per second it’s not suitable in BQ: https://cloud.google.com/bigquery/quotas “A user can make up to 100 API requests per second to an API method” So answer most be B.

MIGs cannot scale the VMs to 0 as per https://cloud.google.com/compute/docs/autoscaler/scaling-cloud-monitoring-metrics#configure_utilization_target So B is the answer.

Cloud Functions can also scale to 0. But I guess because it manageable scaling can be done faster on functions level