Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer Question and Answers

Create an instance template for the instances so VMs have same specs. Set the "ËœAutomatic Restart' to on to VM automatically restarts upon crash. Set the "ËœOn-host maintenance' to Migrate VM instance. This will take care of VM during maintenance window. It will migrate VM instance making it highly available Add the instance template to an instance group so instances can be managed.

• onHostMaintenance: Determines the behavior when a maintenance event occurs that might cause your instance to reboot.

• [Default] MIGRATE, which causes Compute Engine to live migrate an instance when there is a maintenance event.

• TERMINATE, which stops an instance instead of migrating it.

• automaticRestart: Determines the behavior when an instance crashes or is stopped by the system.

• [Default] true, so Compute Engine restarts an instance if the instance crashes or is stopped.

• false, so Compute Engine does not restart an instance if the instance crashes or is stopped.

Enabling automatic restart ensures that compute engine instances are automatically restarted when they crash. And Enabling Migrate VM Instance enables live migrates i.e. compute instances are migrated during system maintenance and remain running during the migration.

Automatic Restart  If your instance is set to terminate when there is a maintenance event, or if your instance crashes because of an underlying hardware issue, you can set up Compute Engine to automatically restart the instance by setting the automaticRestart field to true. This setting does not apply if the instance is taken offline through a user action, such as calling sudo shutdown, or during a zone outage.Ref: https://cloud.google.com/compute/docs/instances/setting-instance-scheduling-options#autorestart

Enabling the Migrate VM Instance option migrates your instance away from an infrastructure maintenance event, and your instance remains running during the migration. Your instance might experience a short period of decreased performance, although generally, most instances should not notice any difference. This is ideal for instances that require constant uptime and can tolerate a short period of decreased performance.Ref: https://cloud.google.com/compute/docs/instances/setting-instance-scheduling-options#live_migrate

Implied rules Every VPC network has two implied firewall rules. These rules exist, but are not shown in the Cloud Console: Implied allow egress rule. An egress rule whose action is allow, destination is 0.0.0.0/0, and priority is the lowest possible (65535) lets any instance send traffic to any destination, except for traffic blocked by Google Cloud. A higher priority firewall rule may restrict outbound access. Internet access is allowed if no other firewall rules deny outbound traffic and if the instance has an external IP address or uses a Cloud NAT instance. For more information, see Internet access requirements. Implied deny ingress rule. An ingress rule whose action is deny, source is 0.0.0.0/0, and priority is the lowest possible (65535) protects all instances by blocking incoming connections to them. A higher priority rule might allow incoming access. The default network includes some additional rules that override this one, allowing certain types of incoming connections. https://cloud.google.com/vpc/docs/firewalls#default_firewall_rules

https://cloud.google.com/network-connectivity/docs/vpn/concepts/best-practices

This aligns with Googles recommended practices. By creating a new project, we achieve complete isolation between development and production environments; as well as isolate this production application from production applications of other departments.

Ref: https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#define-hierarchy

Pub/Sub is a scalable and reliable messaging service that can handle large volumes of data from different sources at different rates. It allows you to decouple the producers and consumers of the data, and provides a durable and persistent storage for the messages until they are delivered. Cloud Functions is a serverless platform that can execute code in response to events, such as messages published to a Pub/Sub topic. It can scale automatically based on the load, and you only pay for the resources you use. By using Pub/Sub and Cloud Functions, you can optimize the storage and processing of the votes, as you can handle the variable rates of incoming votes, process them in real time or near real time, and avoid managing servers or VMs. References:

• Pub/Sub documentation
• Cloud Functions documentation
• Choosing a messaging service for Google Cloud

A simple approach would be to use the command flags available when listing all the IAM policy for a given project. For instance, the following command: `gcloud projects get-iam-policy $PROJECT_ID --flatten="bindings[].members" --format="table(bindings.members)" --filter="bindings.role:roles/owner"` outputs all the users and service accounts associated with the role ‘roles/owner’ in the project in question. https://groups.google.com/g/google-cloud-dev/c/Z6sZs7TvygQ?pli=1

This command correctly specifies the duration that the signed url should be valid for by using the -d flag. The default is 1 hour so omitting the -d flag would have also resulted in the same outcome. Times may be specified with no suffix (default hours), or with s = seconds, m = minutes, h = hours, d = days. The max duration allowed is 7d.Ref: https://cloud.google.com/storage/docs/gsutil/commands/signurl

We need to apply the GCP Best practices. roles/browser Browser Read access to browse the hierarchy for a project, including the folder, organization, and IAM policy. This role doesn't include permission to view resources in the project. https://cloud.google.com/iam/docs/understanding-roles

Quickstart: using the Google Cloud Console

This page shows you how to perform basic tasks in Pub/Sub using the Google Cloud Console.

Note: If you are new to Pub/Sub, we recommend that you start with the interactive tutorial.

Before you begin

Set up a Cloud Console project.

Set up a project

Click to:

Create or select a project.

Enable the Pub/Sub API for that project.

You can view and manage these resources at any time in the Cloud Console.

Install and initialize the Cloud SDK.

Note: You can run the gcloud tool in the Cloud Console without installing the Cloud SDK. To run the gcloud tool in the Cloud Console, use Cloud Shell .

https://cloud.google.com/pubsub/docs/quickstart-console

"The Cloud Spanner to Cloud Storage Text template is a batch pipeline that reads in data from a Cloud Spanner table, optionally transforms the data via a JavaScript User Defined Function (UDF) that you provide, and writes it to Cloud Storage as CSV text files."

https://cloud.google.com/dataflow/docs/guides/templates/provided-batch#cloudspannertogcstext

"The Dataflow connector for Cloud Spanner lets you read data from and write data to Cloud Spanner in a Dataflow pipeline"

https://cloud.google.com/spanner/docs/dataflow-connector

https://cloud.google.com/bigquery/external-data-sources

Set budgets and budget alerts Overview Avoid surprises on your bill by creating Cloud Billing budgets to monitor all of your Google Cloud charges in one place. A budget enables you to track your actual Google Cloud spend against your planned spend. After you've set a budget amount, you set budget alert threshold rules that are used to trigger email notifications. Budget alert emails help you stay informed about how your spend is tracking against your budget. 2. Set budget scope Set the budget Scope and then click Next. In the Projects field, select one or more projects that you want to apply the budget alert to. To apply the budget alert to all the projects in the Cloud Billing account, choose Select all. https://cloud.google.com/billing/docs/how-to/budgets#budget-scop

https://cloud.google.com/bigquery/external-data-sources

An external data source is a data source that you can query directly from BigQuery, even though the data is not stored in BigQuery storage.

BigQuery supports the following external data sources:

Amazon S3

Azure Storage

Cloud Bigtable

Cloud Spanner

Cloud SQL

Cloud Storage

Drive

https://cloud.google.com/compute/docs/access/service-accounts#associating_a_service_account_to_an_instance

To set a default cluster for gcloud commands, run the following command: gcloud config set container/cluster CLUSTER_NAME https://cloud.google.com/kubernetes-engine/docs/how-to/managing-clusters?hl=en

they require cloud storage for archival and the want to automate the process to upload new medical image to cloud storage, hence we go for gsutil to copy on-prem images to cloud storage and automate the process via cron job. whereas Pub/Sub listens to the changes in the Cloud Storage bucket and triggers the pub/sub topic, which is not required.

M1 machine series Medium in-memory databases such as SAP HANA Tasks that require intensive use of memory with higher memory-to-vCPU ratios than the general-purpose high-memory machine types. In-memory databases and in-memory analytics, business warehousing (BW) workloads, genomics analysis, SQL analysis services. Microsoft SQL Server and similar databases.

https://cloud.google.com/compute/docs/machine-types

https://cloud.google.com/compute/docs/machine-types#:~:text=databases%20such%20as-,SAP%20HANA,-In%2Dmemory%20databases

https://www.sap.com/india/products/hana.html#:~:text=is%20SAP%20HANA-,in%2Dmemory,-database%3F

The best option is to instruct the external consultant to generate an SSH key pair, and request the public key from the consultant. Then, add the public key to the instance yourself, and have the consultant access the instance through SSH with their private key. This way, you can grant the consultant access to the instance without requiring a Google account or exposing the instance’s public IP address. This option also follows the best practice of using user-managed SSH keys instead of service account keys for SSH access1.

Option A is not feasible because the external consultant does not have a Google account, and therefore cannot use Identity-Aware Proxy (IAP) to access the instance. IAP requires the user to authenticate with a Google account and have the appropriate IAM permissions to access the instance2. Option B is not secure because it exposes the instance’s public IP address, which can increase the risk of unauthorized access or attacks. Option D is not correct because it reverses the roles of the public and private keys. The public key should be added to the instance, and the private key should be kept by the consultant. Sharing the private key with anyone else can compromise the security of the SSH connection3.

References:

• 1: https://cloud.google.com/compute/docs/instances/adding-removing-ssh-keys
• 2: https://cloud.google.com/iap/docs/using-tcp-forwarding
• 3: https://cloud.google.com/compute/docs/instances/connecting-advanced#sshbetweeninstances

Creating and starting a preemptible VM instance This page explains how to create and use a preemptible virtual machine (VM) instance. A preemptible instance is an instance you can create and run at a much lower price than normal instances. However, Compute Engine might terminate (preempt) these instances if it requires access to those resources for other tasks. Preemptible instances will always terminate after 24 hours. To learn more about preemptible instances, read the preemptible instances documentation. Preemptible instances are recommended only for fault-tolerant applications that can withstand instance preemptions. Make sure your application can handle preemptions before you decide to create a preemptible instance. To understand the risks and value of preemptible instances, read the preemptible instances documentation. https://cloud.google.com/compute/docs/instances/create-start-preemptible-instance

https://cloud.google.com/architecture/identity/migrating-consumer-accounts

Creating scheduled snapshots for persistent disk This document describes how to create a snapshot schedule to regularly and automatically back up your zonal and regional persistent disks. Use snapshot schedules as a best practice to back up your Compute Engine workloads. After creating a snapshot schedule, you can apply it to one or more persistent disks. https://cloud.google.com/compute/docs/disks/scheduled-snapshots

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Since we have a requirement to access data once a quarter and want to go with the most cost-efficient option, we should select Coldline Storage.

Ref: https://cloud.google.com/storage/docs/storage-classes#coldline

Explanation

• Run gcloud builds submit tag gcr.io/img-278322/acme_track_n_trace:v1. is the right answer.

This command correctly tags the image as acme_track_n_trace:v1 and uploads the image to the img-278322 project.

Ref: https://cloud.google.com/sdk/gcloud/reference/builds/submit

https://cloud.google.com/deployment-manager/docs/configuration/create-basic-configuration

Creating or upgrading a cluster by specifying the version as latest does not provide automatic upgrades. Enable node auto-upgrades to ensure that the nodes in your cluster are up-to-date with the latest stable version.

https://cloud.google.com/kubernetes-engine/versioning-and-upgrades

Node auto-upgrades help you keep the nodes in your cluster up to date with the cluster master version when your master is updated on your behalf. When you create a new cluster or node pool with Google Cloud Console or the gcloud command, node auto-upgrade is enabled by default.

Ref: https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-upgrades

https://cloud.google.com/kubernetes-engine/docs/concepts/daemonset

https://cloud.google.com/kubernetes-engine/docs/concepts/daemonset#usage_patterns

DaemonSets attempt to adhere to a one-Pod-per-node model, either across the entire cluster or a subset of nodes. As you add nodes to a node pool, DaemonSets automatically add Pods to the new nodes as needed.

In GKE, DaemonSets manage groups of replicated Pods and adhere to a one-Pod-per-node model, either across the entire cluster or a subset of nodes. As you add nodes to a node pool, DaemonSets automatically add Pods to the new nodes as needed. So, this is a perfect fit for our monitoring pod.

Ref: https://cloud.google.com/kubernetes-engine/docs/concepts/daemonset

DaemonSets are useful for deploying ongoing background tasks that you need to run on all or certain nodes, and which do not require user intervention. Examples of such tasks include storage daemons like ceph, log collection daemons like fluentd, and node monitoring daemons like collectd. For example, you could have DaemonSets for each type of daemon run on all of your nodes. Alternatively, you could run multiple DaemonSets for a single type of daemon, but have them use different configurations for different hardware types and resource needs.

https://cloud.google.com/sdk/gcloud

https://cloud.google.com/sdk/docs/configurations#multiple_configurations

From this source: https://cloud.google.com/billing/docs/how-to/custom-roles#permission_association_and_inheritance

"For example, associating a project with a billing account requires the billing.resourceAssociations.create permission on the billing account and also the resourcemanager.projects.createBillingAssignment permission on the project. This is because project permissions are required for actions where project owners control access, while billing account permissions are required for actions where billing account administrators control access. When both should be involved, both permissions are necessary."

VMs cannot communicate over the internet without a public IP address. Private Google Access permits access to Google APIs and services in Google's production infrastructure. https://cloud.google.com/vpc/docs/private-google-access

https://cloud.google.com/logging/docs/audit

Adding an API as a type provider

This page describes how to add an API to Google Cloud Deployment Manager as a type provider. To learn more about types and type providers, read the Types overview documentation.

A type provider exposes all of the resources of a third-party API to Deployment Manager as base types that you can use in your configurations. These types must be directly served by a RESTful API that supports Create, Read, Update, and Delete (CRUD).

If you want to use an API that is not automatically provided by Google with Deployment Manager, you must add the API as a type provider.

https://cloud.google.com/deployment-manager/docs/configuration/type-providers/creating-type-provider

"large quantity" : Cloud Storage or BigQuery "files" a file is nothing but an Object

Standard persistent disks are efficient and economical for handling sequential read/write operations, but they aren't optimized to handle high rates of random input/output operations per second (IOPS). If your apps require high rates of random IOPS, use SSD persistent disks. SSD persistent disks are designed for single-digit millisecond latencies. Observed latency is application specific.

https://cloud.google.com/spanner/docs/getting-started/set-up

Google Cloud Coldline is a new cold-tier storage for archival data with access frequency of less than once per year. Unlike other cold storage options, Nearline has no delays prior to data access, so now it is the leading solution among competitors.

The Real description is about Coldline storage Class:

Coldline Storage

Coldline Storage is a very-low-cost, highly durable storage service for storing infrequently accessed data. Coldline Storage is a better choice than Standard Storage or Nearline Storage in scenarios where slightly lower availability, a 90-day minimum storage duration, and higher costs for data access are acceptable trade-offs for lowered at-rest storage costs.

Coldline Storage is ideal for data you plan to read or modify at most once a quarter. Note, however, that for data being kept entirely for backup or archiving purposes, Archive Storage is more cost-effective, as it offers the lowest storage costs.

https://cloud.google.com/storage/docs/storage-classes#coldline

https://cloud.google.com/compute/docs/instance-groups/autohealing-instances-in-migs#setting_up_an_autohealing_policy

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

`gcloud services list --available` returns not only the enabled services in the project but also services that CAN be enabled.

https://cloud.google.com/sdk/gcloud/reference/services/list#--available

Run the following command to list the enabled APIs and services in your current project:

gcloud services list

whereas, Run the following command to list the APIs and services available to you in your current project:

gcloud services list –available

https://cloud.google.com/sdk/gcloud/reference/services/list#--available

--available

Return the services available to the project to enable. This list will include any services that the project has already enabled.

To list the services the current project has enabled for consumption, run:

gcloud services list --enabled

To list the services the current project can enable for consumption, run:

gcloud services list –available

Text Description automatically generated with low confidence

Cloud Functions is Google Cloud’s event-driven serverless compute platform. It automatically scales based on the load and requires no additional configuration. You pay only for the resources used.

Ref: https://cloud.google.com/functions

While all other options i.e. Google Compute Engine, Google Kubernetes Engine, Google App Engine support autoscaling, it needs to be configured explicitly based on the load and is not as trivial as the scale up or scale down offered by Google’s cloud functions.

Showing Deployment Manager templates to your team will allow you to define the changes you want to implement in your cloud infrastructure. You can use Cloud Source Repositories to store Deployment Manager templates and collaborate with your team. Cloud Source Repositories are fully-featured, scalable, and private Git repositories you can use to store, manage and track changes to your code.

https://cloud.google.com/source-repositories/docs/features

https://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range

gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetwork gcloud compute networks subnets expand-ip-range NAME --prefix-length=PREFIX_LENGTH [--region=REGION] [GCLOUD_WIDE_FLAG …]

A custom image belongs only to your project. To create an instance with a custom image, you must first have a custom image.

https://developers.google.com/cloud-search/docs/guides/audit-logging-manual

https://cloud.google.com/logging/docs/api/v2/resource-list

GKE Containers have more log than GKE Cluster Operations:

-GKE Containe:

cluster_name: An immutable name for the cluster the container is running in.

namespace_id: Immutable ID of the cluster namespace the container is running in.

instance_id: Immutable ID of the GCE instance the container is running in.

pod_id: Immutable ID of the pod the container is running in.

container_name: Immutable name of the container.

zone: The GCE zone in which the instance is running.

VS

-GKE Cluster Operations

project_id: The identifier of the GCP project associated with this resource, such as "my-project".

cluster_name: The name of the GKE Cluster.

location: The location in which the GKE Cluster is running.

The key to understand the requirement is : "The objects are written once and accessed frequently for 30 days"

Standard Storage

Standard Storage is best for data that is frequently accessed ("hot" data) and/or stored for only brief periods of time.

Archive Storage

Archive Storage is the lowest-cost, highly durable storage service for data archiving, online backup, and disaster recovery. Unlike the "coldest" storage services offered by other Cloud providers, your data is available within milliseconds, not hours or days. Archive Storage is the best choice for data that you plan to access less than once a year.

https://cloud.google.com/storage/docs/storage-classes#standard

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 Storage bucket, which generates Data Access audit logs for the bucket. The generated Data Access audit log has its caller identity redacted.

IAP controls access to your App Engine apps and Compute Engine VMs running on Google Cloud. It leverages user identity and the context of a request to determine if a user should be allowed access. IAP is a building block toward BeyondCorp, an enterprise security model that enables employees to work from untrusted networks without using a VPN.

By default, IAP uses Google identities and IAM. By leveraging Identity Platform instead, you can authenticate users with a wide range of external identity providers, such as:

Email/password

OAuth (Google, Facebook, Twitter, GitHub, Microsoft, etc.)

SAML

OIDC

Phone number

Custom

Anonymous

This is useful if your application is already using an external authentication system, and migrating your users to Google accounts is impractical.

https://cloud.google.com/iap/docs/using-tcp-forwarding#grant-permission

The gcloud projects get-iam-policy command displays the IAM policy for a project, which includes the roles and members assigned to those roles. The Project Owner role grants full access to all resources and actions in the project. By using this command, you can review who has been granted this role and make any necessary changes. References:

• 1: Associate Cloud Engineer Certification Exam Guide | Learn - Google Cloud
• 2: gcloud projects get-iam-policy | Cloud SDK Documentation | Google Cloud
• 3: Understanding roles | Cloud IAM Documentation | Google Cloud

https://cloud.google.com/logging/docs/audit

Data Access 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#data-access

You can use Google Cloud Deployment Manager to create a set of Google Cloud resources and manage them as a unit, called a deployment. For example, if your team's development environment needs two virtual machines (VMs) and a BigQuery database, you can define these resources in a configuration file, and use Deployment Manager to create, change, or delete these resources. You can make the configuration file part of your team's code repository, so that anyone can create the same environment with consistent results. https://cloud.google.com/deployment-manager/docs/quickstart

• The pending Pods resource requests are too large to fit on a single node of the cluster. Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod. is the right answer.

• When you have a deployment with some pods in running and other pods in the pending state, more often than not it is a problem with resources on the nodes. Heres a sample output of this use case. We see that the problem is with insufficient CPU on the Kubernetes nodes so we have to either enable auto-scaling or manually scale up the nodes.

https://cloud.google.com/bigquery/docs/custom-quotas https://cloud.google.com/bigquery/pricing#flat_rate_pricing

A managed instance group (MIG) is a group of identical virtual machines (VMs) that you can manage as a single entity. You can use a MIG to deploy and maintain a stateless application that runs directly on VMs. A MIG can automatically scale the number of VMs based on the load or a schedule. A MIG can also automatically heal the VMs if they become unhealthy or unavailable. A MIG is suitable for applications that need to run on VMs rather than containers or serverless platforms.

B is incorrect because Kubernetes Engine is a managed service for running containerized applications on a cluster of nodes. It is not necessary to use Kubernetes Engine if the application does not use containers and can run directly on VMs.

C is incorrect because Cloud Functions is a serverless platform for running event-driven code in response to triggers. It is not suitable for applications that need to run continuously and handle HTTP requests.

D is incorrect because Cloud Run is a serverless platform for running stateless containerized applications. It is not suitable for applications that do not use containers and can run directly on VMs.

References:

• Managed instance groups documentation
• Choosing a compute option for Google Cloud

You write a lifecycle management rule in XML and push it to the bucket with gsutil lifecycle set config-xml-file. is not right.

gsutil lifecycle set enables you to set the lifecycle configuration on one or more buckets based on the configuration file provided. However, XML is not a valid supported type for the configuration file.

Ref: https://cloud.google.com/storage/docs/gsutil/commands/lifecycle

• Write a script that runs gsutil ls -lr gs://myapp-gcp-ace-logs/** to find and remove items older than 90 days. Repeat this process every morning. is not right.

This manual approach is error-prone, time-consuming and expensive. GCP Cloud Storage provides lifecycle management rules that let you achieve this with minimal effort.

• Write a script that runs gsutil ls -l gs://myapp-gcp-ace-logs/** to find and remove items older than 90 days. Schedule the script with cron. is not right.

This manual approach is error-prone, time-consuming and expensive. GCP Cloud Storage provides lifecycle management rules that let you achieve this with minimal effort.

• Write a lifecycle management rule in JSON and push it to the bucket with gsutil lifecycle set config-json-file. is the right answer.

You can assign a lifecycle management configuration to a bucket. The configuration contains a set of rules which apply to current and future objects in the bucket. When an object meets the criteria of one of the rules, Cloud Storage automatically performs a specified action on the object. One of the supported actions is to Delete objects. You can set up a lifecycle management to delete objects older than 90 days. gsutil lifecycle set enables you to set the lifecycle configuration on the bucket based on the configuration file. JSON is the only supported type for the configuration file. The config-json-file specified on the command line should be a path to a local file containing the lifecycle configuration JSON document.

Ref: https://cloud.google.com/storage/docs/gsutil/commands/lifecycle

Ref: https://cloud.google.com/storage/docs/lifecycle

(https://cloud.google.com/resource-manager/docs/project-migration#change_billing_account )

https://cloud.google.com/billing/docs/concepts

https://cloud.google.com/resource-manager/docs/project-migration

Choosing a region and zone You choose which region or zone hosts your resources, which controls where your data is stored and used. Choosing a region and zone is important for several reasons:

Handling failures

Distribute your resources across multiple zones and regions to tolerate outages. Google designs zones to be independent from each other: a zone usually has power, cooling, networking, and control planes that are isolated from other zones, and most single failure events will affect only a single zone. Thus, if a zone becomes unavailable, you can transfer traffic to another zone in the same region to keep your services running. Similarly, if a region experiences any disturbances, you should have backup services running in a different region. For more information about distributing your resources and designing a robust system, see Designing Robust Systems. Decreased network latency To decrease network latency, you might want to choose a region or zone that is close to your point of service. https://cloud.google.com/compute/docs/regions-zones#choosing_a_region_and_zone