Create reservations on Compute Engine and BigQuery. You can reserve capacity in advance and use it over a period of time. You could also get a cost advantage.
=> There is no need for involved support. It is self-serve via the console.
=> You can reserve resources in advance when you have the need for it. And when you want to take a pay-per-use approach, that is also possible.
=> It is not a good idea to be lock in/hoard resources; you'll pay unnecessarily for resources. Also, it is difficult to time exactly when the demand will be.
References:
https://cloud.google.com/compute/docs/instances/reserving-zonal-resources
https://cloud.google.com/bigquery/docs/reservations-intro

Google Cloud Platform has other storage options to meet your needs for structured, unstructured, transactional and relational data. Core storage options: Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Data Store and Google Big Table. Depending on your application, you might want to use one or several of these services to get the job done.

Compute Engine provides graphics processing units (GPUs) that you can add to your virtual machines (VMs).
You can use these GPUs to accelerate specific workloads on your VMs such as machine learning and data processing.
https://cloud.google.com/compute/docs/gpus

Compute Engine lets you purchase committed use contracts in return for deeply discounted prices for VM usage. These discounts are referred to as committed use discounts. Committed use discounts are ideal for workloads with predictable resource needs. When you purchase a committed use contract, you purchase Compute Engine resources-such as vCPUs, memory, GPUs, local SSDs, and sole-tenant nodes-at a discounted price in return for committing to paying for those resources for 1 year or 3 years. The discount is up to 57% for most resources like machine types or GPUs. The discount is up to 70% for memory-optimized machine types.

The correct answer is B. The policy will be inherited by the projects and their resources within the folder. Here's why:
Context of the Questio n : The organization is deciding on the layout of their resource hierarchy in Google Cloud and wants to understand the impact of applying user access policies at the folder level.
Google Cloud Product Relevance:
In Google Cloud, policies applied at a higher level in the resource hierarchy (like an organization or folder) are inherited by all lower-level resources (such as projects and resources within those projects). Therefore, if a policy is applied to a folder, it will be inherited by all projects and their resources under that folder.
This is a core concept in Google Cloud's Identity and Access Management (IAM) system, which ensures that permissions and policies are consistently applied throughout the resource hierarchy.
Why Not Other Options:
A . The policy applies to the folder only, and will not be inherited by any projects: This is incorrect because IAM policies are inherited down the resource hierarchy.
C . The policy will be applied to all folders within the organization: This is incorrect because the policy applies only to the specific folder and its contents, not to other folders.
D . The policy will be inherited by the projects in the folder but will not affect their resources: This is incorrect because policies inherited by projects also apply to the resources within those projects.
Google Cloud Digital Leader Reference:
Refer to Google Cloud IAM documentation to understand how IAM policies are inherited in a resource hierarchy.