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

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

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

Managed instance groups offer autoscaling capabilities that let you automatically add or delete instances from a managed instance group based on increases or decreases in load (CPU Utilization in this case). Autoscaling helps your apps gracefully handle increases intraffic and reduce costs when the need for resources is lower. You define the autoscaling policy and the autoscaler performs automatic scaling based on the measured load (CPU Utilization in this case). Autoscaling works by adding more instances to your instance group when there is more load (upscaling), and deleting instances when the need for instances is lowered (downscaling). Ref:https://cloud.google.com/compute/docs/autoscaler

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

When setting support levels for permissions in custom roles, you can set to one of SUPPORTED, TESTING or NOT_SUPPORTED.

Ref: https://cloud.google.com/iam/docs/custom-roles-permissions-support

cell phones are sending UDP packets and the only that can receive that type of traffic is a External Network TCP/UDPhttps://cloud.google.com/load-balancing/docs/network

https://cloud.google.com/load-balancing/docs/choosing-load-balancer#lb-decision-tree

What is Google Cloud Memorystore?

Overview. Cloud Memorystore for Redis is a fully managed Redis service for Google Cloud Platform. Applications running on Google Cloud Platform can achieve extreme performance by leveraging the highly scalable, highly available, and secure Redis service without the burden of managing complex Redis deployments.

Folders are nodes in the Cloud Platform Resource Hierarchy. A folder can contain projects, other folders, or a combination of both. Organizations can use folders to group projects under the organization node in a hierarchy. For example, your organization might contain multiple departments, each with its own set of Google Cloud resources. Folders allow you to group these resources on a per-department basis. Folders are used to group resources that share common IAM policies. While a folder can contain multiple folders or resources, a given folder or resource can have exactly one parent.https://cloud.google.com/resource-manager/docs/creating-managing-folders

In general, Google recommends that each instance that needs to call a Google API should run as a service account with the minimum permissions necessary for that instance to do its job. In practice, this means you should configure service accounts for your instances with the following process: Create a new service account rather than using the Compute Engine default service account. Grant IAM roles to that service account for only the resources that it needs. Configure the instance to run as that service account. Grant the instance the https://www.googleapis.com/auth/cloud-platform scope to allow full access to all Google Cloud APIs, so that the IAM permissions of the instance are completely determined by the IAM roles of the service account. Avoid granting more access than necessary and regularly check your service account permissions to make sure they are up-to-date.https://cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances#best_practices

Installing Jenkins

In this section, you use Cloud Marketplace to provision a Jenkins instance. You customize this instance to use the agent image you created in the previous section.

Go to the Cloud Marketplace solution for Jenkins.

Click Launch on Compute Engine.

Change the Machine Type field to 4 vCPUs 15 GB Memory, n1-standard-4.

Machine type selection for Jenkins deployment.

Click Deploy and wait for your Jenkins instance to finish being provisioned. When it is finished, you will see:

Jenkins has been deployed.

https://cloud.google.com/solutions/using-jenkins-for-distributed-builds-on-compute-engine#installing_jenkins

https://cloud.google.com/resource-manager/docs/organization-policy/org-policy-constraints This list constraint defines the set of domains that email addresses added to Essential Contacts can have. By default, email addresses with any domain can be added to Essential Contacts. The allowed/denied list must specify one or more domains of the form @example.com. If this constraint is active and configured with allowed values, only email addresses with a suffix matching one of the entries from the list of allowed domains can be added in Essential Contacts. This constraint has no effect on updating or removing existing contacts. constraints/essentialcontacts.allowedContactDomains

Specifying conditions for alerting policies This page describes how to specify conditions for alerting policies. The conditions for an alerting policy define what is monitored and when to trigger an alert. For example, suppose you want to define an alerting policy that emails you if the CPU utilization of a Compute Engine VM instance is above 80% for more than 3 minutes. You use the conditions dialog to specify that you want to monitor the CPU utilization of a Compute Engine VM instance, and that you want an alerting policy to trigger when that utilization is above 80% for 3 minutes.https://cloud.google.com/monitoring/alerts/ui-conditions-ga

https://cloud.google.com/monitoring/alerts/using-alerting-uihttps://cloud.google.com/monitoring/support/notification-options

App engine is a regional service, which means the infrastructure that runs your app(s) is located in a specific region and is managed by Google to be redundantly available across all the zones within that region. Once an app engine deployment is created in a region, it cant be changed. The only way is to create a new project and create an App Engine instance in europe-west3, send all user traffic to this instance and delete the app engine instance in us-central.

Ref: https://cloud.google.com/appengine/docs/locations

This answer is the most effective way to validate whether the service account used by the CI/CD server has the appropriate roles in the specific project. By checking the IAM roles assigned to the service account, you can see which permissions the service account has and which resources it can access. You can also check if the service account inherits any roles from the folder or organization levels, which may affect its access to the project. You can use the Google Cloud console, the gcloud command-line tool, or the IAM API to view the IAM roles of a service account.

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

performs a peering between the two VPC ' s (the statement makes sure that this option is feasible since it clearly specifies that there is no overlapping between the ip ranges of both vpc ' s), deploy the LoadBalancer as internal with the annotation, and configure the endpoint so that the compute engine instance can access the application internally, that is, without the need to have a public ip at any time and therefore, without the need to go outside the google network. The traffic, therefore, never crosses the public internet.

https://medium.com/pablo-perez/k8s-externaltrafficpolicy-local-or-cluster-40b259a19404

https://cloud.google.com/kubernetes-engine/docs/how-to/internal-load-balancing

clients in a VPC network connected to the LoadBalancer network using VPC Network Peering can also access the Service

https://cloud.google.com/kubernetes-engine/docs/how-to/service-parameters

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.

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

For a web application (typically using HTTP/HTTPS), an Application Load Balancer is the recommended choice as it operates at Layer 7, providing features like content-based routing, SSL termination, and improved security. To expose the application publicly, you would need to use a public DNS zone. An A record in a public DNS zone maps a domain name to the public IP address of the Application Load Balancer. Using a CNAME record would also work but is generally recommended for aliasing one domain name to another, not directly to an IP address.

Option A & B: Network Load Balancers operate at Layer 4 (TCP/UDP) and lack the application-level features of an Application Load Balancer. Private DNS zones are for internal name resolution within your VPC, not for public access.

Option C: While an Application Load Balancer is the correct type, using a private DNS zone wouldn ' t make the web application publicly accessible.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The best practices for load balancing web applications on Google Cloud, including the use of Application Load Balancers for Layer 7 traffic and the configuration of public DNS records (A or CNAME) for public access, are detailed in the Google Cloud Load Balancing and Cloud DNS documentation, both important for the Associate Cloud Engineer certification.

https://cloud.google.com/run/docs/tutorials/pubsub#integrating-pubsub

1. Create a service account. 2. Give the Cloud Run Invoker role to that service account for your Cloud Run application. 3. Create a Cloud Pub/Sub subscription that uses that service account and uses your Cloud Run application as the push endpoint.

1. Create an ingress firewall rule with the following settings: " ¢ Targets: all instances with tier #2 service account " ¢ Source filter: all instances with tier #1 service account " ¢ Protocols: allow TCP:8080 2. Create an ingress firewall rule with the following settings: " ¢ Targets: all instances with tier #3 service account " ¢ Source filter: all instances with tier #2 service account " ¢ Protocols: allow TCP: 8080

gcloud compute networks subnets expand-ip-range NAME gcloud compute networks subnets expand-ip-range - expand the IP range of a Compute Engine subnetworkhttps://cloud.google.com/sdk/gcloud/reference/compute/networks/subnets/expand-ip-range

The correct answer is A because the application only needs to create new log objects in one specific bucket. A dedicated service account gives the workload its own identity, and granting roles/storage.objectCreator on the bucket limits access to the minimum scope. Google Cloud documents that Storage Object Creator allows object creation but does not grant permission to view, delete, or overwrite objects, and the role can be granted at the bucket level. Object Admin and Storage Admin are excessive because they allow broader object or bucket administration. Using the default Compute Engine service account at project scope is also too broad. Generating a service account key is unnecessary and weakens security.

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

Cloud SQL Auth Proxy: This proxy ensures secure connections to your Cloud SQL database by automatically handling encryption (SSL/TLS) and IAM-based authentication. It simplifies the management of secure connections without needing to manage SSL/TLS certificates manually. IAM Database Authentication: This allows you to use IAM credentials to authenticate to the database, providing a unified and secure authentication mechanism that integrates seamlessly with Google Cloud IAM.

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

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

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

The correct answer is C because the resources must be centrally managed under a single Google Cloud organization, but each startup requires separate billing and invoicing. Folders provide administrative separation inside one organization, while separate Cloud Billing accounts allow costs to be tracked and invoiced separately. Google Cloud Billing accounts define who pays for linked resources, track charges for projects linked to that billing account, and invoiced accounts result in invoices per Cloud Billing account. A single shared billing account would not satisfy the requirement for separate monthly invoices. Creating multiple organizations would violate the requirement for centralized management under one Google Cloud organization.

Our requirement is to follow Google recommended practices to achieve the end result. Configuring Private Google Access for On-Premises Hosts is best achieved by VPN/Interconnect + Advertise Routes + Use restricted Google IP Range.

Using Cloud VPN or Interconnect, create a tunnel to a VPC in GCP

Using Cloud Router to create a custom route advertisement for 199.36.153.4/30. Announce that network to your on-premises network through the VPN tunnel.

In your on-premises network, configure your DNS server to resolve *.googleapis.com as a CNAME to restricted.googleapis.com is the right answer right, and it is what Google recommends.

Ref: https://cloud.google.com/vpc/docs/configure-private-google-access-hybrid

You must configure routes so that Google API traffic is forwarded through your Cloud VPN or Cloud Interconnect connection, firewall rules on your on-premises firewall to allow the outgoing traffic, and DNS so that traffic to Google APIs resolves to the IP range youve added to your routes.

You can use Cloud Router Custom Route Advertisement to announce the Restricted Google APIs IP addresses through Cloud Router to your on-premises network. The Restricted Google APIs IP range is 199.36.153.4/30. While this is technically a public IP range, Google does not announce it publicly. This IP range is only accessible to hosts that can reach your Google Cloud projects through internal IP ranges, such as through a Cloud VPN or Cloud Interconnect connection. Without having a public IP address or access to the internet, the only way you could connect to cloud storage is if you have an internal route to it.

So Negotiate with the security team to be able to give public IP addresses to the servers is not right. Following Google recommended practices is synonymous with using Googles services (Not quite, but it is  at least for the exam !!).

So In this VPC, create a Compute Engine instance and install the Squid proxy server on this instance is not right.

Migrating the VM to Compute Engine is a bit drastic when Google says it is perfectly fine to have Hybrid Connectivity architectures https://cloud.google.com/hybrid-connectivity.

So,

Use Migrate for Compute Engine (formerly known as Velostrata) to migrate these servers to Compute Engine is not right.

When you intially click on Monitoring(Stackdriver Monitoring) it creates a workspac(a stackdriver account) linked to the ACTIVE(CURRENT) Project from which it was clicked.

Now if you change the project and again click onto Monitoring it would create an another workspace(a stackdriver account) linked to the changed ACTIVE(CURRENT) Project, we don ' t want this as this would not consolidate our result into a single dashboard(workspace/stackdriver account).

If you have accidently created two diff workspaces merge them under Monitoring > Settings > Merge Workspaces > MERGE.

If we have only one workspace and two projects we can simply add other GCP Project under

Monitoring > Settings > GCP Projects > Add GCP Projects.

https://cloud.google.com/monitoring/settings/multiple-projects

Nothing about groupshttps://cloud.google.com/monitoring/settings?hl=en

The correct answer is B because the metric needed is application-specific: the number of new database connections created by the application. Cloud Run instance creation is not the same as database connection creation, so option A measures the wrong thing. Cloud SQL logs may show database-side activity, but they do not reliably express the application’s connection reuse behavior in the required form. Cloud Trace is for request tracing and latency analysis, not counting newly created database connections. By logging each new connection in the application and creating a log-based metric, Cloud Monitoring can chart the exact behavior. Google Cloud supports user-defined log-based metrics for project-specific application or business metrics.

https://cloud.google.com/iam/docs/best-practices-for-using-and-managing-service-accounts

If an application uses third-party or custom identities and needs to access a resource, such as a BigQuery dataset or a Cloud Storage bucket, it must perform a transition between principals. Because Google Cloud APIs don ' t recognize third-party or custom identities, the application can ' t propagate the end-user ' s identity to BigQuery or Cloud Storage. Instead, the application has to perform the access by using a different Google identity.

1. In Stackdriver Logging, create a filter to view only Compute Engine logs. 2. Click Create Export. 3. Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.

https://cloud.google.com/monitoring/alerts#alerting-example

Google Cloud Storage Triggers

Cloud Functions can respond to change notifications emerging from Google Cloud Storage. These notifications can be configured to trigger in response to various events inside a bucket—object creation, deletion, archiving and metadata updates.

Note: Cloud Functions can only be triggered by Cloud Storage buckets in the same Google Cloud Platform project.

Event types

Cloud Storage events used by Cloud Functions are based on Cloud Pub/Sub Notifications for Google Cloud Storage and can be configured in a similar way.

Supported trigger type values are:

google.storage.object.finalize

google.storage.object.delete

google.storage.object.archive

google.storage.object.metadataUpdate

Object Finalize

Trigger type value: google.storage.object.finalize

This event is sent when a new object is created (or an existing object is overwritten, and a new generation of that object is created) in the bucket.

https://cloud.google.com/functions/docs/calling/storage#event_types

https://cloud.google.com/iam/docs/job-functions/auditing#scenario_external_auditors

Because if you directly add users to the IAM roles, then if any users left the organization then you have to remove the users from multiple places and need to revoke his/her access from multiple places. But, if you put a user into a group then its very easy to manage these type of situations. Now, if any user left then you just need to remove the user from the group and all the access got revoked

The organization creates a Google group for these external auditors and adds the current auditor to the group. This group is monitored and is typically granted access to the dashboard application. During normal access, the auditors ' Google group is only granted access to view the historic logs stored in BigQuery. If any anomalies are discovered, the group is granted permission to view the actual Cloud Logging Admin Activity logs via the dashboard ' s elevated access mode. At the end of each audit period, the group ' s access is then revoked. Data is redacted using Cloud DLP before being made accessible for viewing via the dashboard application. The table below explains IAM logging roles that an Organization Administrator can grant to the service account used by the dashboard, as well as the resource level at which the role is granted.

auto-provisioning = Attaches and deletes node pools to cluster based on the requirements. Hence creating a GPU node pool, and auto-scaling would be betterhttps://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-provisioning

IP 10.0.3.21 is internal by default, and to ensure that it will be static non-changing it should be selected as static internal ip address.

Shared VPC allows an organization to connect resources from multiple projects to a common Virtual Private Cloud (VPC) network, so that they can communicate with each other securely and efficiently using internal IPs from that network. When you use Shared VPC, you designate a project as a host project and attach one or more other service projects to it. The VPC networks in the host project are called Shared VPC networks. Eligible resources from service projects can use subnets in the Shared VPC network

https://cloud.google.com/vpc/docs/shared-vpc

" For example, an existing instance in a service project cannot be reconfigured to use a Shared VPC network, but a new instance can be created to use available subnets in a Shared VPC network. "

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

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

gcloud deployment-manager deployments create creates deployments based on the configuration file. (Infrastructure as code). All the configuration related to the artifacts is in the configuration file. This command correctly creates a cluster based on the provided cluster.yaml configuration file.

Ref: https://cloud.google.com/sdk/gcloud/reference/deployment-manager/deployments/create

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.

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.

Cloud Debugger helps inspect the state of an application, at any code location, without stopping or slowing down the running app //https://cloud.google.com/debugger/docs

You can create two configurations – one for the development project and another for the production project. And you do that by running “gcloud config configurations create” command.

https://cloud.google.com/sdk/gcloud/reference/config/configurations/create

In your custom script, you can load these configurations one at a time and execute gcloud compute instances list to list Google Compute Engine instances in the project that is active in the gcloud configuration.

Ref: https://cloud.google.com/sdk/gcloud/reference/compute/instances/list

Once you have this information, you can export it in a suitable format to a suitable target e.g. export as CSV or export to Cloud Storage/BigQuery/SQL, etc

`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

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/iap/docs/using-tcp-forwarding

Create an instance template for the instances so VMs have same specs. Set the " ˜Automatic Restart ' to on to VM automatically restarts upon crash. Set the " ˜On-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

Keyword need to look for

- " High Throughput " ,

- " Consistent " ,

- " Property based data insert/fetch like ngine status, distance traveled, fuel level, and more. " which can be designed in column,

- " Large Scale Customer Base + Each Customer has multiple sensor which send event in seconds " This will go for pera bytes situation,

- Export data based on the time of the event.

- Atomic

o BigTable will fit all requirement.

o DataStore is not fully Atomic

o CloudStorage is not a option where we can export data based on time of event. We need another solution to do that

o FireStore can be used with MobileSDK.

The correct answer is C because Cloud Monitoring already collects Compute Engine CPU utilization metrics and supports alerting policies with thresholds and duration windows. The requirement is to notify the operations team when average CPU utilization exceeds 75% for five continuous minutes, which maps directly to a Cloud Monitoring metric-based alert policy. Logs-based metrics are unnecessary because CPU utilization is not primarily a log event and is already available as a platform metric. Installing agents and writing custom polling scripts adds operational overhead and is less scalable. Autoscaling policies adjust capacity, but they do not replace alerting policies and do not provide the requested notification workflow by themselves. Cloud Monitoring is the native, low-maintenance solution.

https://gtseres.medium.com/using-service-accounts-across-projects-in-gcp-cf9473fef8f0

You create the service account in proj-sa and take note of the service account email, then you go to proj-vm in IAM > ADD and add the service account ' s email as new member and give it the Compute Storage Admin role.

https://cloud.google.com/compute/docs/access/iam#compute.storageAdmin

Keyword, Version, traffic splitting, App Engine supports traffic splitting for versions before releasing.

Comprehensive and Detailed In Depth Explanation:

The key requirement is to migrate applications that rely on hard-coded internal IP addresses without modifying the application code. To achieve this, the migrated VMs in Google Cloud need to retain their original internal IP addresses.

A. Non-overlapping CIDR ranges and new static IPs: This option requires changing the IP addresses of the migrated workloads, which would necessitate modifying the application code to reflect these new addresses. This violates a core requirement.

B. Migrating DNS and using ephemeral IPs: While migrating DNS can be beneficial in the long run, using ephemeral internal IP addresses for the migrated workloads means their IPs could change upon restart, breaking the hard-coded IP address dependencies.

C. Single subnet with Cloud NAT and static NAT IP: Cloud NAT allows instances without external IP addresses to access the internet, but it doesn ' t help in preserving the internal IP addresses that the applications use to communicate with each other. The internal IP addresses of the VMs would still be within the VPC subnet range and might conflict if they are the same as the on-premises IPs.

D. Same CIDR ranges and same static IPs: Creating a VPC with the same CIDR ranges as the on-premises network and assigning the same static internal IP addresses to the migrated workloads is the only way to ensure that the applications can continue to communicate using their hard-coded IP addresses without any code changes. This approach effectively extends the on-premises network ' s IP address space into Google Cloud (though without direct connectivity initially, as stated in the problem). Once the workloads are migrated, future steps can involve establishing connectivity (e.g., using VPN or Interconnect) if needed for hybrid scenarios.

Google Cloud Documentation References:

VPC Network Overview: https://cloud.google.com/vpc/docs/vpc - This document explains the fundamentals of VPC networks and their IP addressing. While it doesn ' t explicitly detail lift-and-shift scenarios with identical IP ranges without connectivity, it lays the groundwork for understanding VPC configuration.

Considerations for planning IP address ranges: https://cloud.google.com/vpc/docs/subnets#ip-ranges - This section discusses IP address planning, and while overlapping ranges are generally discouraged for connected networks, for isolated migration scenarios as described, it ' s a necessary step to avoid application changes. The problem statement explicitly says the environments are not connected during the initial migration.

===========

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

Coldline Storage is the perfect service to store audit logs from all the projects and is very cost-efficient as well. Coldline Storage is a very low-cost, highly durable storage service for storing infrequently accessed data.

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

HTTP(S) load balancing is a Google-recommended practice for distributing web traffic across multiple regions and zones, and providing high availability, scalability, and security for web applications. It supports both IPv4 and IPv6 addresses, and can handle SSL/TLS termination and encryption. It also integrates with Cloud CDN, Cloud Armor, and Cloud Identity-Aware Proxy for enhanced performance and protection. A MIG can be used as a backend service for HTTP(S) load balancing, and can automatically scale and heal the VM instances that host the web application.

To configure DNS for HTTP(S) load balancing, you need to create an A record in your DNS public zone with the load balancer’s IP address. This will map your domain name to the load balancer’s IP address, and allow users to access your web application using the domain name. A CNAME record is not recommended, as it can cause latency and DNS resolution issues. A private zone is not suitable, as it is only visible within your VPC network, and not to the public internet.

HTTP(S) Load Balancing documentation

Setting up DNS records for HTTP(S) load balancing

Choosing a load balancer

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/storage/docs/object-versioning

The correct answer is D because Cloud Run is designed to run containerized applications directly from container images and can scale down to zero when there is no traffic. It is also the fastest production path because it removes the need to manage servers, clusters, instance templates, or Kubernetes manifests. A managed instance group behind a load balancer does not scale to zero in the same serverless way and requires more infrastructure work. App Engine Flexible runs containers but does not match the direct Artifact Registry-to-serverless deployment pattern as cleanly. GKE gives control, but it requires cluster and Kubernetes resource management and is slower operationally. Cloud Run satisfies both speed and scale-to-zero requirements.

The correct answer is D because the team already uses GKE and wants the same declarative model and tooling used for application deployments. Config Connector lets teams manage Google Cloud resources as Kubernetes custom resources. This means infrastructure resources can be declared in Kubernetes manifests and applied through Kubernetes-native workflows. The Google Cloud console is manual and not declarative. Shell scripts with gcloud commands are imperative, harder to audit, and less aligned with Kubernetes deployment workflows. Terraform is declarative and valid for infrastructure management, but it uses separate tooling and state management. The question specifically asks for the same declarative approach and tooling as application deployments, making Config Connector the strongest match.

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

The correct answer is C because Cloud Asset Inventory can search resources across an organization and filter by asset type and creation time. The asset type iam.googleapis.com/ServiceAccountKey directly targets service account keys, which is exactly what must be audited. Using organization scope is important because the question says there are hundreds of service accounts across applications, likely distributed across projects. Cloud KMS keys are encryption keys, not service account keys, so option A searches the wrong resource type. Listing service accounts only identifies service accounts, not their individual keys or key ages. API keys are different credentials from service account keys, so option D also targets the wrong asset type. Cloud Asset Inventory is the most scalable audit method here.

The correct answer is C because the requirement is strict least privilege. The operations team only needs three actions: start, stop, and reset existing Compute Engine VM instances. A custom IAM role is the correct mechanism when predefined roles are too broad. Google Cloud custom roles are designed so administrators can include only the permissions required for a specific operational task, and the role definition explicitly lists included permissions. Viewer cannot start, stop, or reset instances, so B is not functional. Instance Admin is too broad because it includes administrative permissions beyond the stated task. IAM Conditions cannot simply subtract a single permission from an overprivileged predefined role.

The Vertical Pod Autoscaler (VPA) in Kubernetes automatically adjusts the CPU and memory requests and limits of the containers within a pod based on historical and real-time resource usage. In this scenario, where a single-replica stateful application needs more CPU during peak times, VPA can dynamically increase the CPU allocated to the pod when needed and potentially decrease it during off-peak periods to optimize resource utilization and cost efficiency.

Option A: Cluster autoscaling adds or removes nodes in your GKE cluster based on the resource requests of your pods. While it can help with overall cluster capacity, it oesn ' t directly address the need for more CPU for a specific pod.

Option C: Horizontal Pod Autoscaler (HPA) scales the number of pod replicas based on observed CPU utilization or other select metrics. Since the application can only have one replica, HPA is not suitable.

Option D: Node auto-provisioning is similar to cluster autoscaling, automatically creating and deleting node pools based on workload demands. It doesn ' t directly manage the resources of individual pods.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The functionality and use cases of the Vertical Pod Autoscaler (VPA) are detailed in the Google Kubernetes Engine documentation, specifically within the resource management and autoscaling sections. Understanding how VPA can dynamically adjust pod resources is relevant to the Associate Cloud Engineer certification.

https://cloud.google.com/sql/docs/postgres

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

The correct answer is A because the requirement is company-wide access for employees in one Cloud Identity domain, not public or cross-domain access. The BigQuery Data Viewer role provides read-only access to dataset resources, including reading and querying table data when granted at the dataset level. Using the domain principal domain:terramearth.com grants access to authenticated users in that organization’s domain. allUsers would include unauthenticated public access, and allAuthenticatedUsers would include any authenticated Google account, which is far broader than the company boundary. A service account represents an application identity, not all human employees.

/25:

CIDR to IP Range

Result

CIDR Range 172.16.20.128/25

Netmask 255.255.255.128

Wildcard Bits 0.0.0.127

First IP 172.16.20.128

First IP (Decimal) 2886734976

Last IP 172.16.20.255

Last IP (Decimal) 2886735103

Total Host 128

CIDR

172.16.20.128/25

/24:

CIDR to IP Range

Result

CIDR Range 172.16.20.128/24

Netmask 255.255.255.0

Wildcard Bits 0.0.0.255

First IP 172.16.20.0

First IP (Decimal) 2886734848

Last IP 172.16.20.255

Last IP (Decimal) 2886735103

Total Host 256

CIDR

172.16.20.128/24

https://cloud.google.com/kuberun/docs/architecture-overview#components_in_the_default_installation

The correct answer is D because Google-recommended IAM administration favors group-based access rather than assigning roles directly to individual users. Cloud Identity groups make onboarding and offboarding simpler: when a person joins or leaves a team, administrators update group membership instead of editing IAM bindings across hundreds of resources. Security groups are specifically intended for access-control use cases and are better suited for IAM policies than ordinary discussion-only groups. Service accounts should represent workloads, not teams of human users. Granting humans permission to impersonate shared service accounts weakens accountability, and allowing users to generate service account keys is even worse because it introduces long-lived credentials. Security-labeled Cloud Identity groups provide scalable, auditable access management.

The requirement is for a stable set of egress IP addresses from a GKE cluster for allowlisting by a third party, following best practices.

Option A is not recommended: Using a single node lacks scalability and high availability. Relying on a single node ' s static IP creates a single point of failure and doesn ' t align with GKE ' s design principles. Disabling autoscaling hinders elasticity.

Option C is complex and unreliable: Public nodes typically have ephemeral external IPs (unless manually configured per node, which is difficult to manage with autoscaling). Dynamically tracking and emailing IPs daily is operationally burdensome and prone to race conditions where the allowlist might lag behind IP changes.

Option D uses Cloud NAT but with dynamic IPs. Dynamic IPs change over time, making them unsuitable for stable firewall allowlists.

Option B is the Google-recommended practice: Configuring the GKE cluster with private nodes enhances security as nodes don ' t have direct external IPs. Cloud NAT provides managed network address translation for these private nodes to access the internet. By configuring Cloud NAT with a static allocation of external IP addresses, all egress traffic from the private GKE nodes will appear to originate from this stable, predictable set of IPs. This set can be given to the vendor for allowlisting without worrying about node IP changes due to scaling or maintenance.

This approach decouples the application ' s egress IP from the individual nodes, providing stability and adhering to the principle of least privilege (private nodes).

Database Center is the correct choice because it is designed to provide a centralized view of Google Cloud database and data storage services, including operational health and availability-related insights. The requirement is not only to list assets but to monitor the health of multiple managed data services and identify downtime risk. Dataplex Universal Catalog is mainly for data discovery, governance, and metadata search, not operational database health monitoring. Terraform state only reflects resources managed by Terraform and does not provide live service health or risk signals. Cloud Asset Inventory can show resource inventory, but it is not the best operational console for database health. Database Center gives the unified operational overview required with the least manual effort.

The correct answer is D because Kubernetes provides a direct rollback command for failed or problematic rollouts. In GKE, deployments are managed using standard Kubernetes deployment mechanics, so kubectl rollout undo is the correct command to return a workload to the previous rollout revision. Kubernetes documentation describes kubectl rollout undo as the command used to roll back to a previous rollout and gives deployment rollback examples. Manually scaling old and new Pods is risky because it bypasses deployment controller state and can create configuration drift. Deleting the deployment causes service disruption and removes the workload object. kubectl rollout restart restarts Pods using the current deployment specification; it does not revert to a previous revision.

An external HTTP(S) load balancer is a Google-recommended solution for distributing web traffic across multiple regions and zones, and providing high availability, scalability, and security for web applications. It supports both IPv4 and IPv6 addresses, and can handle SSL/TLS termination and encryption. It also integrates with Cloud CDN, Cloud Armor, and Cloud Identity-Aware Proxy for enhanced performance and protection. A managed instance group (MIG) can be used as a backend service for the HTTP(S) load balancer, and can automatically scale the number of VM instances based on the CPU load. A Cloud Storage bucket can also be used as a backend service for the HTTP(S) load balancer, and can serve static content such as images, videos, or HTML files. A URL map can be used to route requests to different backend services based on the path or host of the request. For example, a URL map can send requests for /static/* to the Cloud Storage bucket, and requests for /dynamic/* to the MIG. A managed SSL certificate can be used to secure the connection between the clients and the load balancer, and can be automatically provisioned and renewed by Google.

A is incorrect because an internal HTTP(S) load balancer is only visible within a VPC network, and not to the public internet. It is used for internal applications that need to communicate with other internal services. Identity-Aware Proxy is a service that provides secure access to web applications without using a VPN. It is not a load balancer, and it does not distribute user traffic.

B is incorrect because installing HTTPS certificates on the instance is not necessary, as the HTTP(S) load balancer can handle SSL/TLS termination and encryption. It is also more complex and less secure to manage the certificates on the instance level, as they need to be updated and synchronized across multiple instances.

D is incorrect because an external network load balancer is a TCP/UDP load balancer that operates at the network layer. It is not suitable for web applications that use HTTP(S) protocols, as it does not support SSL/TLS termination and encryption, URL maps, or Cloud Storage backends. It is also less efficient and scalable to forward the requests to the Cloud Storage from the web servers, as it adds an extra hop and latency.

HTTP(S) Load Balancing documentation

Setting up HTTP(S) Load Balancing with Cloud Storage

Creating and using SSL certificates

Choosing a load balancer

as gcloud config configurations list can help check for the existing configurations and activate can help switch to the configuration.

gcloud config configurations list  lists existing named configurations

gcloud config configurations activate  activates an existing named configuration

Obtains access credentials for your user account via a web-based authorization flow. When this command completes successfully, it sets the active account in the current configuration to the account specified. If no configuration exists, it creates a configuration named default.

A tag is simply a character string added to a tags field in a resource, such as Compute Engine virtual machine (VM) instances or instance templates. A tag is not a separateresource, so you cannot create it separately. All resources with that string are considered to have that tag. Tags enable you to make firewall rules and routes applicable to specific VM instances.

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

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.

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