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

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

https://cloud.google.com/compute/docs/instances/connecting-to-windows#remote-desktop-connection-app

https://cloud.google.com/compute/docs/instances/windows/generating-credentials

https://cloud.google.com/compute/docs/instances/connecting-to-windows#before-you-begin

Best practices for persistent disk snapshots

You can create persistent disk snapshots at any time, but you can create snapshots more quickly and with greater reliability if you use the following best practices.

Creating frequent snapshots efficiently

Use snapshots to manage your data efficiently.

Create a snapshot of your data on a regular schedule to minimize data loss due to unexpected failure.

Improve performance by eliminating excessive snapshot downloads and by creating an image and reusing it.

Set your snapshot schedule to off-peak hours to reduce snapshot time.

Snapshot frequency limits

Creating snapshots from persistent disks

You can snapshot your disks at most once every 10 minutes. If you want to issue a burst of requests to snapshot your disks, you can issue at most 6 requests in 60 minutes.

If the limit is exceeded, the operation fails and returns the following error:

https://cloud.google.com/compute/docs/disks/snapshot-best-practices

https://cloud.google.com/architecture/best-practices-vpc-design#shared-service Cloud VPN is another alternative. Because Cloud VPN establishes reachability through managed IPsec tunnels, it doesn 't have the aggregate limits of VPC Network Peering. Cloud VPN uses a VPN Gateway for connectivity and doesn't consider the aggregate resource use of the IPsec peer. The drawbacks of Cloud VPN include increased costs (VPN tunnels and traffic egress), management overhead required to maintain tunnels, and the performance overhead of IPsec.

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

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

`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

https://cloud.google.com/storage/docs/lifecycle#setstorageclass-cost

# The object's time spent set at the original storage class counts towards any minimum storage duration that applies for the new storage class.

Logged information Within Cloud Audit Logs, there are two types of logs: Admin Activity logs: Entries for operations that modify the configuration or metadata of a project, bucket,or object. Data Access logs: Entries for operations that modify objects or read a project, bucket, or object. There are several sub-types of data access logs: ADMIN_READ: Entries for operations that read the configuration or metadata of a project, bucket, or object. DATA_READ: Entries for operations that read an object. DATA_WRITE: Entries for operations that create or modify an object.https://cloud.google.com/storage/docs/audit-logs#types

https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-autoscaler#adding_a_node_pool_with_autoscaling

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

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.

When applied to a dataset, this role provides the ability to read the dataset's metadata and list tables in the dataset. When applied to a project, this role also provides the ability to run jobs, including queries, within the project. A member with this role can enumerate their own jobs, cancel their own jobs, and enumerate datasets within a project. Additionally, allows the creation of new datasets within the project; the creator is granted the BigQuery Data Owner role (roles/bigquery.dataOwner) on these new datasets.

https://cloud.google.com/bigquery/docs/access-control

A deployment is responsible for keeping a set of pods running. A service is responsible for enabling network access to a set of pods.

https://cloud.google.com/sql/docs/mysql/backup-recovery/backups#can_i_export_a_backup

https://cloud.google.com/sql/docs/mysql/import-export#automating_export_operations

C:\GCP\appeng>gcloud config list

[core]

account = xxx@gmail.com

disable_usage_reporting = False

project = my-first-demo-xxxx

https://cloud.google.com/endpoints/docs/openapi/troubleshoot-gce-deployment

https://cloud.google.com/storage/docs/object-versioning

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

Navigate to Stackdriver Logging and select resource.labels.project_id=*. is not right.

Log entries are held in Stackdriver Logging for a limited time known as the retention period  which is 30 days (default configuration). After that, the entries are deleted. To keep log entries longer, you need to export them outside of Stackdriver Logging by configuring log sinks.

Ref: https://cloud.google.com/blog/products/gcp/best-practices-for-working-with-google-cloud-audit-logging

Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days. is not right.

While this works, it makes no sense to use Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery when Google provides a feature (export sinks) that does exactly the same thing and works out of the box.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days. is not right.

You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.

Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks

Either way, we now have the data in Cloud Storage, but querying logs information from Cloud Storage is harder than Querying information from BigQuery dataset. For this reason, we should prefer Big Query over Cloud Storage.

Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days. is the right answer.

You can export logs by creating one or more sinks that include a logs query and an export destination. Supported destinations for exported log entries are Cloud Storage, BigQuery, and Pub/Sub.

Ref: https://cloud.google.com/logging/docs/export/configure_export_v2

Sinks are limited to exporting log entries from the exact resource in which the sink was created: a Google Cloud project, organization, folder, or billing account. If it makes it easier to exporting from all projects of an organication, you can create an aggregated sink that can export log entries from all the projects, folders, and billing accounts of a Google Cloud organization.

Ref: https://cloud.google.com/logging/docs/export/aggregated_sinks

Either way, we now have the data in a BigQuery Dataset. Querying information from a Big Query dataset is easier and quicker than analyzing contents in Cloud Storage bucket. As our requirement is to Quickly analyze the log contents, we should prefer Big Query over Cloud Storage.

Also, You can control storage costs and optimize storage usage by setting the default table expiration for newly created tables in a dataset. If you set the property when the dataset is created, any table created in the dataset is deleted after the expiration period. If you set the property after the dataset is created, only new tables are deleted after the expiration period.

For example, if you set the default table expiration to 7 days, older data is automatically deleted after 1 week.

Ref: https://cloud.google.com/bigquery/docs/best-practices-storage

An external passthrough Network Load Balancer operates at layer 4 (TCP/UDP) and preserves the original client IP address. This is a key requirement stated in the question. It's also designed for external-facing applications that need to handle TCP or UDP traffic directly without proxying at the application layer.

Option A: An internal passthrough Network Load Balancer is used for load balancing traffic within a Virtual Private Cloud (VPC) network, not for exposing applications to the public internet.

Option C & D: Global and regional external proxy Network Load Balancers operate at layer 7 (HTTP/HTTPS) or use a proxy for TCP/SSL, which means they terminate the client connection at the load balancer and establish a new connection to the backend instances. This does not preserve the original client IP address by default (though there are ways to retrieve it via headers, it's not a direct passthrough).

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The different types of Google Cloud Load Balancers, their operating layers (L4 vs. L7), and their capabilities, including client IP preservation, are thoroughly documented in the Google Cloud Load Balancing documentation, a critical area for the Associate Cloud Engineer certification. The distinction between passthrough and proxy load balancers is essential.

This command correctly deletes the deployment. Pods are managed by kubernetes workloads (deployments). When a pod is deleted, the deployment detects the pod is unavailable and brings up another pod to maintain the replica count. The only way to delete the workload is by deleting the deployment itself using the kubectl delete deployment command.

$ kubectldeletedeployment nginx

deployment.apps nginx deleted

Ref: https://kubernetes.io/docs/reference/kubectl/cheatsheet/#deleting-resources

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

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

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

This command correctly lists pods that have the label app=prod. When creating the deployment, we used the label app=prod so listing pods that have this label retrieve the pods belonging to nginx deployments. You can list pods by using Kubernetes CLI  kubectl get pods.

Ref: https://kubernetes.io/docs/tasks/access-application-cluster/list-all-running-container-images/

Ref: https://kubernetes.io/docs/tasks/access-application-cluster/list-all-running-container-images/#list-containers-filtering-by-pod-label

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.

===========

Signed URLs are used to give time-limited resource access to anyone in possession of the URL, regardless of whether they have a Google account.https://cloud.google.com/storage/docs/access-control/signed-urls

GKE Autopilot is a mode of operation in GKE where Google manages the underlying infrastructure, including nodes, node pools, and their upgrades. This significantly reduces the management and operational overhead for the user, allowing teams to focus solely on deploying and managing their containerized applications. Since the applications are not exposed publicly, the zonal or regional nature of the cluster primarily impacts availability within Google Cloud, and Autopilot is available for both. Autopilot minimizes the operational burden, which is a key requirement.

Option A: A Standard zonal GKE cluster requires you to manage the nodes yourself, including sizing, scaling, and upgrades, increasing operational overhead compared to Autopilot.

Option B: Manually installing and managing Kubernetes on a fleet of Compute Engine instances involves the highest level of management overhead, which contradicts the requirement to minimize it.

Option D: A Standard regional GKE cluster provides higher availability than a zonal cluster by replicating the control plane and nodes across multiple zones within a region. However, it still requires you to manage the underlying nodes, unlike Autopilot.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The different modes of GKE operation, including Standard and Autopilot, and their respective management responsibilities and benefits, are clearly outlined in the Google Kubernetes Engine documentation, a core topic for the Associate Cloud Engineer certification. The emphasis on reduced operational overhead with Autopilot is a key differentiator.

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

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

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

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

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.

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

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.

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/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.

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/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.

The best option is to attach a single service account to the compute instances and add minimal rights to the service account. Then, allow the service account to impersonate a Cloud Identity user with elevated permissions to create, update, or delete resources. This way, the service account can use short-lived access tokens to authenticate to Google Cloud APIs without needing to manage service account keys. This option follows the principle of least privilege and reduces the risk of credential leakage and misuse.

Option A is not recommended because it requires human intervention, which can slow down the CI/CD pipeline and introduce human errors. Option C is not secure because it grants all required IAM permissions to a single service account, which can increase the impact of a compromised key. Option D is not cost-effective because it requires creating and managing multiple service accounts and keys, as well as using a secret manager service.

1: https://cloud.google.com/iam/docs/impersonating-service-accounts

2: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys

3: https://cloud.google.com/iam/docs/understanding-service-accounts

Let's analyze each option based on the requirements: PostgreSQL compatibility, significant performance improvement, minimal schema/code changes, handling large data volumes, Google-recommended practices, and cost minimization:

A. Migrate your data to AlloyDB: AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service that offers significant performance improvements over standard PostgreSQL due to its architectural optimizations. It is designed to handle large data volumes and minimizes the need for schema and application code changes as it's wire-compatible with PostgreSQL. This aligns well with the requirements for performance improvement, minimal changes, large data, and being a Google-recommended option for PostgreSQL workloads.

B. Migrate your data to Spanner: Spanner is a globally distributed, horizontally scalable database with strong consistency. While it offers excellent scalability and performance, it's not directly PostgreSQL-compatible. Migrating to Spanner would likely require significant schema and application code changes due to differences in data modeling and SQL dialect.

C. Migrate your data to Firebase: Firebase is a suite of mobile and web development tools, with its primary database offering being Firestore (a NoSQL document database) and Realtime Database. These are not PostgreSQL-compatible and would require substantial changes to the data model and application code.

D. Migrate your data to Bigtable: Bigtable is a highly scalable NoSQL wide-column store. It's not compatible with PostgreSQL and requires a completely different data model and application logic.

Therefore, AlloyDB is the most suitable option as it provides PostgreSQL compatibility for minimal migration effort, significant performance improvements, scalability for large data volumes, and is a recommended Google Cloud database service for PostgreSQL workloads.

Google Cloud Documentation References:

AlloyDB for PostgreSQL Overview: https://cloud.google.com/alloydb/docs/overview - This document highlights AlloyDB 's PostgreSQL compatibility, performance benefits, scalability, and suitability for migrating existing PostgreSQL workloads.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - This emphasizes Spanner 's unique features and differences from traditional relational databases like PostgreSQL.

Firebase Documentation: https://firebase.google.com/docs - This outlines the features of Firebase, including Firestore and Realtime Database, highlighting their NoSQL nature and incompatibility with PostgreSQL.

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This describes Bigtable as a NoSQL database, emphasizing its differences from relational databases like PostgreSQL.

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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

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.

Keywords: - continually -> Standard - mission-critical analytics -> dual-regional

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

https://cloud.google.com/spanner/docs/cpu-utilization#recommended-max

The goal is to create a serverless, scalable, and asynchronous transaction processing system with minimal management overhead.

Serverless Requirement:Options involving installing Kafka on VMs (A, B) or using VM instances for processing (B, C) introduce management overhead associated with VMs (patching, scaling configuration, OS management) and Kafka cluster management, violating the serverless and minimal management criteria.

Asynchronous Requirement:Both Kafka and Pub/Sub can handle asynchronous messaging. However, Pub/Sub is Google Cloud's fully managed, serverless messaging service, inherently minimizing management overhead compared to self-managed Kafka on VMs.

Scalability and Processing:Cloud Run is a fully managed, serverless platform that automatically scales based on traffic, suitable for processing transactions without managing underlying infrastructure. VM instances require manual scaling configuration or managed instance groups, adding overhead.

Combining Pub/Sub for asynchronous message ingestion (fully managed, serverless) and Cloud Run for processing (fully managed, serverless, scalable) directly meets all requirements: serverless, scalable, asynchronous, and minimal management overhead. Option D is the only one that uses fully serverless components for both ingestion and processing.

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

https://cloud.google.com/compute/docs/instance-groups/rolling-out-updates-to-managed-instance-groups#max_unavailable

Let's analyze each option to find the one that meets the requirements of no startup time for new traffic, two idle instances, and minimal administrative overhead:

A. Unchecking "Serve this revision immediately" and using a traffic simulation tool: Unchecking "Serve this revision immediately" does prevent the new revision from receiving traffic immediately. However, manually using a traffic simulation tool adds administrative overhead. It also doesn't guarantee that two idle instances will be ready before traffic is shifted; you would need to monitor and adjust traffic manually based on the simulation.

B. Configuring service autoscaling and setting the minimum number of instances to 2: Service-level autoscaling applies to all revisions of the service. Setting the minimum instances at the service level would ensure at least two instances are running across all active revisions, not specifically for the new revision before traffic shift.

C. Configuring revision autoscaling for the new revision and setting the minimum number of instances to 2: This is the correct approach. By configuring revision autoscaling specifically for the new revision and setting the minimum number of instances to 2, Cloud Run will ensure that at least two instances of the new version are running and ready to serve traffic before you redirect any traffic to it. This eliminates startup latency when you do shift traffic. It also minimizes administrative overhead as Cloud Run manages the instance scaling based on this configuration.

D. Configuring revision autoscaling for the existing revision and setting the minimum number of instances to 2: This would ensure the existing version has at least two idle instances, which doesn't directly address the requirement of having idle instances ready for the new version before traffic redirection.

Google Cloud Documentation References:

Cloud Run Autoscaling: https://cloud.google.com/run/docs/configuring/min-instances - This document explains how to configure minimum and maximum instances for Cloud Run services and revisions. It clarifies that you can set minimum instances at the revision level to ensure instances are always ready.

Cloud Run Traffic Management: https://cloud.google.com/run/docs/managing/traffic - This describes how to deploy new revisions and gradually shift traffic between them. Combining minimum instances on the new revision with traffic splitting allows for zero-downtime deployments with pre-warmed instances.

===========

Let's evaluate each option based on the requirement of sub-millisecond latency for globally stored IoT data:

A. Spanner with Caching: While Spanner offers strong consistency and global scalability, the base latency might not consistently be sub-millisecond for all read/write operations globally. Introducing caching adds complexity and doesn't guarantee sub-millisecond latency for all initial reads or cache misses.

B. Bigtable: Bigtable is a highly scalable NoSQL database service designed for low-latency, high-throughput workloads. It excels at storing and retrieving large volumes of time-series data, which is typical for IoT sensor data. Its architecture is optimized for single-key lookups and scans, providing consistent sub-millisecond latency, making it a strong candidate for this use case.

C. BigQuery: BigQuery is a fully managed, serverless data warehouse designed for analytical queries on large datasets. While it's excellent for analyzing IoT data in batch, it's not optimized for the low-latency, high-throughput ingestion and retrieval required for real-time IoT applications with sub-millisecond latency needs.

D. Cloud Storage with Cloud CDN: Cloud Storage is object storage and is not designed for low-latency transactional workloads. Cloud CDN is a content delivery network that caches content closer to users for faster delivery, but it's not suitable for the primary storage of rapidly incoming IoT sensor data requiring sub-millisecond write latency.

Google Cloud Documentation References:

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This document highlights Bigtable 's suitability for low-latency and high-throughput applications, including IoT. It mentions its ability to handle massive amounts of data with consistent performance.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - While Spanner offers low latency, Bigtable is generally preferred for extremely high-throughput, low-latency use cases like raw sensor data ingestion due to its optimized architecture for such workloads.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - This emphasizes BigQuery 's analytical capabilities rather than low-latency operational workloads.

Cloud Storage Overview: https://cloud.google.com/storage/docs/overview - This describes Cloud Storage as object storage, not ideal for sub-millisecond latency reads and writes required for real-time IoT data.

===========

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/resource-manager/docs/project-migration#oauth_consent_screen

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

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

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).

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/compute/docs/troubleshooting/troubleshooting-migs

https://cloud.google.com/compute/docs/instance-templates#how_to_update_instance_templates

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

https://cloud.google.com/run/docs/tutorials/gcloud

https://cloud.google.com/resource-manager/docs/creating-managing-projects

https://cloud.google.com/iam/docs/understanding-roles#primitive_roles

You can shut down projects using the Cloud Console. When you shut down a project, this immediately happens: All billing and traffic serving stops, You lose access to the project, The owners of the project will be notified and can stop the deletion within 30 days, The project will be scheduled to be deleted after 30 days. However, some resources may be deleted much earlier.

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

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

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

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

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 associatedwith the role ‘roles/owner’ in the project in question.https://groups.google.com/g/google-cloud-dev/c/Z6sZs7TvygQ?pli=1

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

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

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/spanner/docs/getting-started/set-up

When we create subnets in the same VPC with different CIDR ranges, they can communicate automatically within VPC. Resources within a VPC network can communicatewith one another by using internal (private) IPv4 addresses, subject to applicable network firewall rules

Ref: https://cloud.google.com/vpc/docs/vpc