The requirement is for an infrastructure-as-code (IaC) solution that can manage Kubernetes resources and other cloud resources across multiple cloud providers (Google Cloud and two others).
Option A (YAML manifests): YAML manifests are primarily used for defining Kubernetes objects, not for provisioning general cloud resources (like VPCs, IAM policies, databases) across different cloud providers.
Option C (Python + SDKs): While possible, writing custom scripts using each provider's SDK requires significant development effort to handle state management, dependencies, and provider differences. It essentially reinvents much of what dedicated IaC tools provide and is not a standard IaC approach.
Option D (Config Connector): Config Connector allows managing Google Cloud resources using Kubernetes- style manifests and APIs. It is specific to Google Cloud and cannot manage resources in other cloud providers.
Option B (Terraform): Terraform is an open-source IaC tool explicitly designed for building, changing, and versioning infrastructure safely and efficiently across multiple cloud providers and on-premises data centers.
It uses providers for different platforms (GCP, AWS, Azure, Kubernetes, etc.), allowing a unified workflow to manage diverse resources across the required environments (Google Cloud, other clouds, Kubernetes).
Terraform is the standard tool for multi-cloud IaC automation as described in the scenario.
References:
Terraform on Google Cloud: "Terraform is an open source infrastructure as code (IaC) tool... Terraform lets you manage Google Cloud resources with declarative configuration files..." - https://cloud.google.com/docs
/terraform
Terraform Providers (General): Terraform supports numerous providers for various cloud platforms and services. - https://registry.terraform.io/browse/providers Config Connector Overview: "Config Connector is a Kubernetes addon that allows you to manage Google Cloud resources through Kubernetes." (Google Cloud specific) - https://cloud.google.com/config-connector
/docs/overview

Spot VMs (formerly known as preemptible VMs) are Compute Engine virtual machine instances that are available at a much lower price than standard Compute Engine instances. However, Compute Engine might preempt (stop) these instances if it needs to reclaim those resources for other tasks. This makes Spot VMs ideal for batch processing jobs that are fault-tolerant and can handle interruptions, as they can be restarted when resources become available again. This directly addresses the requirement for a cost-effective solution for interruptible workloads.
Option A: While containerization offers portability and consistency, it doesn't inherently provide cost savings for compute resources. You would still need to choose a cost-effective underlying compute option.
Option B: Custom machine types allow you to tailor the CPU and memory configuration of your VMs, which can optimize costs to some extent by avoiding over-provisioning. However, they don't offer the significant cost reduction that Spot VMs provide.
Option C: The M1 machine series is a specific family of Compute Engine instances optimized for memory- intensive workloads. While potentially suitable for the job's requirements, it doesn't inherently address the cost-effectiveness requirement as directly as Spot VMs, which are priced lower regardless of the machine series.
Reference to Google Cloud Certified - Associate Cloud Engineer Documents:
The concept and use cases for Spot VMs are explicitly covered in the Compute Engine section of the Google Cloud documentation, which is a key area for the Associate Cloud Engineer certification. The cost savings and suitability for fault-tolerant workloads are highlighted as primary benefits.

You can use the Google Cloud Pricing Calculator to total the estimated monthly costs for each GCP product.
You dont incur any charges for doing so.
Ref: https://cloud.google.com/products/calculator

* Given that the new instance wants to access the application on the existing compute engine instance, these applications seem to be related so they should be within the same VPC. It is possible to have them in different VPCs and peer the VPCs but this is a lot of additional work and we can simplify this by choosing the option below (which is the answer)
1. Create a subnet in the same VPC, in europe-west1.
2. Create the new instance in the new subnet and use the first instance subnets private address as the endpoint.
is the right answer.
* We can create another subnet in the same VPC and this subnet is located in europe-west1. We can then spin up a new instance in this subnet. We also have to set up a firewall rule to allow communication between the two subnets. All instances in the two subnets with the same VPC can communicate through the internal IP Address Ref: https://cloud.google.com/vpc