Free Practice Questions for Google Associate-Cloud-Engineer Exam

In Google compute engine, if predefined machine types don’t meet your needs, you can create an instance with custom virtualized hardware settings. Specifically, you can create an instance with a custom number of vCPUs and custom memory, effectively using a custom machine type. Custom machine types are ideal for the following scenarios:

   1. Workloads that aren’t a good fit for the predefined machine types that are available to you.

   2. Workloads that require more processing power or more memory but don’t need all of the upgrades that are provided by the next machine type level.

In our scenario, we only need a memory upgrade. Moving to a bigger instance would also bump up the CPU which we don’t need so we have to use a custom machine type. It is not possible to change memory while the instance is running so you need to first stop the instance, change the memory and then start it again. See below a screenshot that shows how CPU/Memory can be customized for an instance that has been stopped.

Ref: https://cloud.google.com/compute/docs/instances/creating-instance-with-custom-machine-type

This is the most optimal solution. Rather than recreating all nodes, you create a new node pool with GPU enabled. You then modify the pod specification to target particular GPU types by adding node selector to your workloads Pod specification. YOu still have a single cluster so you pay Kubernetes cluster management fee for just one cluster thus minimizing the cost.

Ref: https://cloud.google.com/kubernetes-engine/docs/how-to/gpus

Ref: https://cloud.google.com/kubernetes-engine/pricing

Example:

apiVersion: v1

kind: Pod

metadata:

name:my-gpu-pod

spec:

containers:

name:my-gpu-container

image: nvidia/cuda:10.0-runtime-ubuntu18.04

command: [/bin/bash]

resources:

limits:

nvidia.com/gpu: 2

nodeSelector:

cloud.google.com/gke-accelerator: nvidia-tesla-k80# or nvidia-tesla-p100 or nvidia-tesla-p4 or nvidia-tesla-v100 or nvidia-tesla-t4

https://cloud.google.com/vpc/docs/vpc#manually_created_subnet_ip_ranges

https://cloud.google.com/storage/docs/parallel-composite-uploads

https://cloud.google.com/storage/docs/uploads-downloads#parallel-composite-uploads

The goal is to create a landing zone facilitating private IP communication across production projects and apply organization-wide firewall rules, following best practices and minimizing operational costs.

Network Structure:Individual VPCs with Peering (A, B): While VPC Peering allows private connectivity, managing a full mesh or complex peering topology across many projects becomes operationally complex and can hit peering limits. It's not the recommended pattern for centralized connectivity in a landing zone.

Shared VPC (C, D): This is the Google-recommended practice for scenarios where resources from multiple projects need to communicate privately within a common VPC network. A central host project owns the network, and service projects use it. This simplifies network administration and connectivity.

Firewall Rules:Organization Policies (A, C): These enforce organizational constraints (e.g., disable external IPs, restrict locations) but do not define specific network firewall rules (like allowing TCP ports).

Hierarchical Firewall Policies (B, D): These allow defining firewall rules at the Organization or Folder level, which are inherited by resources in descendant projects/folders. This is the mechanism to apply consistent firewall rules (like allowing specific TCP ports) across all VMs in the organization (or a specific folder) efficiently, without managing rules in each individual VPC or project.

Combining Shared VPC for the network structure (best practice for cross-project private communication and central management) with Hierarchical Firewall Policies (for applying organization-wide firewall rules) meets all requirements efficiently and follows Google recommendations.  

https://cloud.google.com/appengine/docs/standard/python/splitting-traffic#gcloud

https://cloud.google.com/spanner/docs/iam#spanner.databaseUser

Using the gcloud tool, execute the gcloud iam roles describe roles/spanner.databaseUser command on Cloud Shell. Attach the users to a newly created Google group and add the group to the role.

Types of audit logs Cloud Audit Logs provides the following audit logs for each Cloud project, folder, and organization: Admin Activity audit logs Data Access audit logs System Event audit logs Policy Denied audit logs *Data Access audit logs contain API calls that read the configuration or metadata of resources, as well as user-driven API calls that create, modify, or read user-provided resource data.https://cloud.google.com/logging/docs/audit#types

https://cloud.google.com/logging/docs/audit#data-access Cloud Storage: When Cloud Storage usage logs are enabled, Cloud Storage writes usage data to the Cloud Storagebucket, which generates Data Access audit logs for the bucket. The generated Data Access audit log has its caller identity redacted.

As mentioned in Schema and data model, you should be careful when choosing a primary key to not accidentally create hotspots in your database. One cause of hotspots is having a column whose value monotonically increases as the first key part, because this results in all inserts occurring at the end of your key space. This pattern is undesirable because Cloud Spanner divides data among servers by key ranges, which means all your inserts will be directed at a single server that will end up doing all the work.https://cloud.google.com/spanner/docs/schema-design#primary-key-prevent-hotspots