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

"Google Cloud VPC Network Peering allows internal IP address connectivity across two Virtual Private Cloud (VPC) networks regardless of whether they belong to the same project or the same organization."

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

while

"Cloud Interconnect provides low latency, high availability connections that enable you to reliably transfer data between your on-premises and Google Cloud Virtual Private Cloud (VPC) networks."

https://cloud.google.com/network-connectivity/docs/interconnect/concepts/overview

and

"HA VPN is a high-availability (HA) Cloud VPN solution that lets you securely connect your on-premises network to your VPC network through an IPsec VPN connection in a single region."

https://cloud.google.com/network-connectivity/docs/vpn/conce pts/overview

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.

Comprehensive and Detailed In Depth Explanation:

The goal is to proactively identify stuck Dataflow jobs with minimal management effort. Let's analyze each option:

A. Error Reporting for slowdowns: Error Reporting primarily focuses on capturing and aggregating exceptions and errors (stack traces). While a stuck job might eventually throw an error, it might also just become unresponsive without generating explicit errors. Relying solely on Error Reporting might not provide timely detection of stuck jobs. Identifying stack traces that indicate slowdowns can also be complex and require significant manual configuration and analysis.

B. Personalized Service Health dashboard: The Personalized Service Health dashboard provides information about Google Cloud service incidents that might be affecting your resources. While it can alert you to broader Dataflow service outages, it won't specifically identify individual stuck jobs due to application-level errors or logic within your Dataflow pipeline.

C. Pub/Sub messages for delays, backup job, and Cloud Tasks alerts: This approach involves significant custom implementation and management. You would need to instrument your Dataflow jobs to detect delays, send messages to Pub/Sub, manage a backup job, and configure Cloud Tasks for alerting. This adds considerable operational overhead and complexity.

D. Cloud Monitoring alerts on data freshness metric: Dataflow provides built-in metrics, including "data freshness" (or similar metrics like "system lag" or "processing time"), which indicate how far behind the pipeline is in processing data. If a job gets stuck, the data freshness will deteriorate beyond an acceptable threshold. Cloud Monitoring allows you to easily set up alerts based on these built-in metrics. This requires minimal custom coding and leverages the platform's existing monitoring capabilities, aligning with the "minimal management effort" requirement.

Therefore, setting up Cloud Monitoring alerts on relevant Dataflow metrics like data freshness is the most efficient and recommended way to detect stuck Dataflow jobs with minimal management overhead.

Google Cloud Documentation References:

Monitoring Dataflow Pipelines: https://cloud.google.com/dataflow/docs/guides/monitoring-your-pipeline - This document details the various metrics available for monitoring Dataflow jobs in Cloud Monitoring, including metrics related to processing time, system lag, and data freshness.

Creating Alerts in Cloud Monitoring: https://cloud.google.com/monitoring/alerts/create - Explains how to set up alerts based on metrics collected by Cloud Monitoring.

Dataflow Metrics: https://cloud.google.com/dataflow/docs/reference/monitoring-metrics - Provides a comprehensive list of Dataflow metrics that can be used for monitoring and alerting.

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

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

Quickstart: using the Google Cloud Console

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

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

Before you begin

Set up a Cloud Console project.

Set up a project

Click to:

Create or select a project.

Enable the Pub/Sub API for that project.

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

Install and initialize the Cloud SDK.

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

https://cloud.go ogle.com/pubsub/docs/quickstart-console

Granting more permissions than necessary violates the principle of least privilege, a fundamental security best practice. While option A grants the necessary permissions (as subsets exist in two predefined roles), it might also grant more permissions than the Operations team strictly requires for their tasks on GKE and Cloud SQL. Option D is too broad; 'Admin' roles grant extensive permissions that likely exceed the specific needs.

Google Cloud's best practices strongly recommend adhering to the principle of least privilege. Creating a custom role allows you to precisely define the set of permissions the Operations team needs for their specific tasks on the GKE cluster and the Cloud SQL instance, without granting any unnecessary permissions. This minimizes the potential blast radius in case of accidental or malicious actions.

Google Cloud Documentation References:

IAM best practices: https://cloud.google.com/iam/docs/best-practices - This document explicitly recommends granting the minimum necessary permissions.

Creating and managing custom roles: https://cloud.google.com/iam/docs/creating-managing-custom-roles - This explains how to create roles tailored to specific job functions.

Understanding roles: https://cloud.google.com/iam/docs/understanding-roles - This outlines the concepts of predefined and custom roles and their use cases.

===========

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

Billing Administrators can not create a new billing account, and the project is presumably already created. Project Billing Manager allows you to link the created billing account to the project. It is vague on how the billing account gets created but by process of elimination

Ref: https://cloud.google.com/compute/docs/machine-types#n1_machine_type

https://cloud.google.com/bigquery/docs/quickstarts/quickstart-web-ui?authuser=4

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

https://cloud.google.com/storage/docs/access-control/iam-roles

Logged onto console and followed the steps and was able to see all the assigned users and roles.

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

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

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

Explanation

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

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

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

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

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

https://cloud.google.com/kuberun/docs/rollouts-rollbacks-traffic-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

You can use the Google Cloud Pricing Calculator to total the estimated monthly costs for each GCP product. You dont incur any charges for doing so.

Ref: https://cloud.google.com/products/calculator

Cloud Storage allows world-wide storage and retrieval of any amount of data at any time. We dont need to set up auto-scaling ourselves. Cloud Storage autoscaling is managed by GCP. Cloud Storage is an object store so it is suitable for storing photos. Cloud Storage allows world-wide storage and retrieval so cater well to our worldwide audience. Cloud storage provides us lifecycle rules that can be configured to automatically delete objects older than 30 days. This also fits our requirements. Finally, Google Cloud Storage offers several storage classes such as Nearline Storage ($0.01 per GB per Month) Coldline Storage ($0.007 per GB per Month) and Archive Storage ($0.004 per GB per month) which are significantly cheaper than any of the options above.

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

Ref: https://cloud.google.com/storage/pricing

You can generate Windows passwords using either the Google Cloud Console or the gcloud command-line tool. This option uses the right syntax to reset the windows password.

gcloud compute reset-windows-password windows-instance

Ref: https://cloud.google.com/compute/docs/instances/windows/creating-passwords-for-windows-instances#gcloud

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

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://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_Types#importance_of_setting_the_correct_m ime_type

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

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

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

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

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

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

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

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

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

Text Description automatically generated with low confidence

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

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

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

https://cloud.google.com/compute/docs/autoscaler#specifications

Autoscaling works independently from autohealing. If you configure autohealing for your group and an instance fails the health check, the autohealer attempts to recreate the instance. Recreating an instance can cause the number of instances in the group to fall below the autoscaling threshold (minNumReplicas) that you specify.

Since we need the application running at all times, we need a minimum 1 instance.

Only a single instance of the VM should run, we need a maximum 1 instance.

We want the application running at all times. If the VM crashes due to any underlying hardware failure, we want another instance to be added to MIG so that application can continue to serve requests. We can achieve this by enabling autoscaling. The only option that satisfies these three is Set autoscaling to On, set the minimum number of instances to 1, and then set the maximum number of instances to 1.

Ref: https://cloud.google.com/compute/docs/autoscaler

"Run gcloud configurations list to start the Compute Engine instances". How the heck are you expecting to "start" GCE instances doing "configuration list".

Each gcloud configuration has a 1 to 1 relationship with the region (if a region is defined). Since we have two different regions, we would need to create two separate configurations using gcloud config configurations create

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

Secondly, you can activate each configuration independently by running gcloud config configurations activate [NAME]

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

Finally, while each configuration is active, you can run the gcloud compute instances start [NAME] command to start the instance in the configurations region.

https://cloud.google.com/sdk/gcloud/reference/compute/in stances/start

Before you run the gcloud compute instances list command, you need to do two things: authenticate with your user account and set the default project for gcloud CLI.

To authenticate with your user account, you need to run gcloud auth login, enter your login credentials in the dialog window, and paste the received login token to gcloud CLI. This will authorize the gcloud CLI to access Google Cloud resources on your behalf1.

To set the default project for gcloud CLI, you need to run gcloud config set project $my_project, where $my_project is the ID of the project that contains the instances you want to list. This will saveyou from having to specify the project flag for every gcloud command2.

Option B is not recommended, because using a service account key increases the risk of credential leakage and misuse. It is also not necessary, because you can use your user account to authenticate to the gcloud CLI3. Option C is not correct, because there is no such thing as a Cloud Identity user account key. Cloud Identity is a service that provides identity and access management for Google Cloud users andgroups4. Option D is not required, because the gcloud compute instances list command does not depend on the default zone. You can list instances from all zones or filter by a specific zone using the --filter flag.

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, itoesn'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.

UESTION NO: 48 of 50

(You host your website on Compute Engine. The number of global users visiting your website is rapidly expanding. You need to minimize latency and support user growth in multiple geographical regions. You also want to follow Google-recommended practices and minimize operational costs. Which two actions should you take?

Choose 2 answers)

A. Deploy all of your VMs in a single Google Cloud region with the largest available CIDR range.

B. Deploy your VMs in multiple Google Cloud regions closest to your users’ geographical locations.

C. Use an external Application Load Balancer in Regional mode.

D. Use an external Application Load Balancer in Global mode.

E. Use a Network Load Balancer.

Answer: BD

To minimize latency for a global user base, it's crucial to serve users from regions geographically close to them. Deploying VMs in multiple Google Cloud regions (Option B) achieves this by reducing the network distance and thus the round-trip time for requests.

To support user growth and provide a single point of entry with global reach, a global external Application Load Balancer (Option D) is the recommended choice for web applications. It distributes traffic to backend instances across multiple regions based on user proximity, capacity, and health. Application Load Balancers also offer features like SSL termination, content-based routing, and security policies, which are important for modern web applications.

* Option A: Deploying in a single region, regardless of the CIDR range, will result in high latency for users far from that region.

* Option C: A regional external Application Load Balancer only distributes traffic within a single region, not across multiple global regions, thus not effectively minimizing latency for all global users.

* Option E: Network Load Balancers operate at Layer 4 and don't offer the application-level routing and features of an Application Load Balancer, which are generally preferred for web applications. While they can be global, Application Load Balancers are better suited for this scenario.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The concepts of multi-region deployments for low latency and the use of global load balancers (specifically Application Load Balancers for web traffic) for global reach and traffic management are core topics in the Compute Engine and Load Balancing sections of the Google Cloud documentation, which are essential for the Associate Cloud Engineer certification. The best practices for global application deployment are emphasized.

roles/monitoring.viewer provides read-only access to get and list information about all monitoring data and configurations. This role provides monitoring access and fits our requirements. roles/monitoring.viewer. is the right answer.

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

GCP App Engine natively offers traffic splitting functionality between versions. You can use traffic splitting to specify a percentage distribution of traffic across two or more of the versions within a service. Splitting traffic allows you to conduct A/B testing between your versions and provides control over the pace when rolling out features.

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

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.

"...we recommend that you enable Cloud Billing data export to BigQuery at the same time that you create a Cloud Billing account. "https://cloud.google.com/billing/docs/how-to/export-data-bigquery

https://medium.com/google-cloud/analyzing-google-cloud-billing-data-with-big-query-30bae1c2aae4

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

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

https://cloud.google.com/config-connector/docs/how-to/secrets#gcloud

Configure the ACLs on each image in Cloud Storage to give read-only access to the default Compute Engine service account. is not right.

As mentioned above, Container Registry ignores permissions set on individual objects within the storage bucket so this isnt going to work.

Ref: https://cloud.google.com/container-registry/docs/access-control

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

Comprehensive and Detailed In Depth Explanation:

Let's evaluate each database option against the requirements: automatic scaling, multi-region support, and low latency for a growing social media platform:

A. Bigtable: Bigtable is a highly scalable NoSQL database designed for large analytical and operational workloads with low latency. It offers excellent horizontal scalability and can be deployed across multiple regions for high availability and lower latency for a global user base. However, it's a NoSQL database and might require significant changes to your existing PostgreSQL data model and application code.

B. BigQuery: BigQuery is a fully managed, serverless data warehouse optimized for analytical queries on large datasets. It's not designed for low-latency transactional workloads that a social media platformwould require for real-time user interactions. While it's globally available, its primary use case is not operational database needs.

C. Spanner: Spanner is a globally distributed, horizontally scalable relational database service with strong consistency. It offers automatic scaling, built-in multi-region and multi-continental configurations for high availability and low latency across a global user base, and supports standard SQL (with some extensions). This makes it a strong candidate for a rapidly growing platform needing scalability, global presence, and low latency. While it's not directly PostgreSQL, it offers a relational model and tools to aid migration.

D. Cloud SQL for PostgreSQL: Cloud SQL offers managed PostgreSQL instances with automatic scaling capabilities. It supports high availability within a region and cross-region read replicas for disaster recovery and read scaling. However, its multi-region capabilities for write operations and automatic scaling across regions are more limited compared to Spanner. For a rapidly growing platform with a primarily North American user base but future global expansion in mind and a need for low latency, Spanner's architecture is better suited for true multi-region write capabilities and consistent low latency globally.

Considering the requirements for automatic scaling, multi-region support for both reads and writes with low latency for a growing user base, Spanner is the most appropriate choice.

Google Cloud Documentation References:

Cloud Spanner Overview: https://cloud.google.com/spanner/docs/overview - This document highlights Spanner's global scalability, strong consistency, and multi-region capabilities.

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - While scalable and low-latency, it's a NoSQL database, which might require significant application changes.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - Focuses on analytics, not low-latency transactional workloads.

Cloud SQL for PostgreSQL Overview: https://cloud.google.com/sql/docs/postgres/overview - While it offers scaling and regional HA, its multi-region write capabilities are not as robust as Spanner's.

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(A) Object Lifecycle Management

Delete

The Delete action deletes an object when the object meets all conditions specified in the lifecycle rule.

Exception: In buckets with Object Versioning enabled, deleting the live version of an object causes it to become a noncurrent version, while deleting a noncurrent version deletes that version permanently.

https://cloud.google.com/storage/docs/lifecycle#delete

(B) Signed URLs

This page provides an overview of signed URLs, which you use 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

https://cloud.google.c om/vpc/docs/vpc#manually_created_subnet_ip_ranges

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.

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.

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

Using primitive roles The following table lists the primitive roles that you can grant to access a project, the description of what the role does, and the permissions bundled within that role. Avoid using primitive roles except when absolutely necessary. These roles are very powerful, and include a large number of permissions across all Google Cloud services. For more details on when you should use primitive roles, see the Identity and Access Management FAQ. IAM predefined roles are much more granular, and allow you to carefully manage the set of permissions that your users have access to. See Understanding Roles for a list of roles that can be granted at the project level. Creating custom roles can further increase the control you have over user permissions.https://cloud.google.com/resource-manager/docs/access-control-proj#using_primitive_roles

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

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/n etworks/subnets/expand-ip-range

Spot VMs (formerly known as preemptible VMs) are Compute Engine virtual machine instances that are available at a much lower price than standard Compute Engine instances. However, Compute Engine might preempt (stop) these instances if it needs to reclaim those resources for other tasks. This makes Spot VMs ideal for batch processing jobs that are fault-tolerant and can handle interruptions, as they can be restarted when resources become available again. This directly addresses the requirement for a cost-effective solution for interruptible workloads.

Option A: While containerization offers portability and consistency, it doesn't inherently provide cost savings for compute resources. You would still need to choose a cost-effective underlying compute option.

Option B: Custom machine types allow you to tailor the CPU and memory configuration of your VMs, which can optimize costs to some extent by avoiding over-provisioning. However, they don't offer the significant cost reduction that Spot VMs provide.

Option C: The M1 machine series is a specific family of Compute Engine instances optimized for memory-intensive workloads. While potentially suitable for the job's requirements, it doesn't inherently address the cost-effectiveness requirement as directly as Spot VMs, which are priced lower regardless of the machine series.

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

The concept and use cases for Spot VMs are explicitly covered in the Compute Engine section of the Google Cloud documentation, which is a key area for the Associate Cloud Engineer certification. The cost savings and suitability for fault-tolerant workloads are highlighted as primary benefits.

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.

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

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.

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

Autopilot is more reliable and stable release gives more time to fix issues in new version of GKE

https://console.cloud.google.com/marketplace/details/gc-launcher-for-mongodb-atlas/mongodb-atlas