Google Associate-Cloud-Engineer Google Cloud Certified - Associate Cloud Engineer Exam Practice Test

Read the dataset's metadata and to list tables in the dataset. Read data and metadata from the dataset's tables. When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

BigQuery Data Viewer

(roles/bigquery.dataViewer)

When applied to a table or view, this role provides permissions to:

Read data and metadata from the table or view.

This role cannot be applied to individual models or routines.

When applied to a dataset, this role provides permissions to:

Read the dataset's metadata and list tables in the dataset.

Read data and metadata from the dataset's tables.

When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

Lowest-level resources where you can grant this role:

Table

View

BigQuery Job User

(roles/bigquery.jobUser)

Provides permissions to run jobs, including queries, within the project.

Lowest-level resources where you can grant this role:

Project

to run jobs https://cloud.google.com/bigquery/docs/access-control#bigquery.jobUser databaseUser needs additional role permission to run jobshttps://cloud.google.com/spanner/docs/iam#spanner.databaseUser

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

Comprehensive and Detailed In Depth Explanation:

The problem states that a subnet is nearing its IP address capacity, and the requirement is to expand it without downtime and with minimal operational cost, following Google-recommended practices.

A. Creating a second VPC with the same subnet IP range and peering: While VPC Network Peering allows communication between VPCs, having overlapping IP ranges is generally not recommended and can lead to routing complexities if not managed carefully. It also adds operational overhead of managing two VPCs. This is not the most straightforward or cost-effective solution for simply expanding IP capacity within the same logical network.

B. Deleting and recreating the subnet: Deleting a subnet that contains active VM instances will cause downtime for those instances, violating a key requirement.

C. Using the Google Cloud CLI tool to expand the primary IP range of your subnet: Google Cloud allows you to expand the primary IP range of an existing subnet after it's created, as long as there are no conflicting subnets in the VPC. This operation does not require deleting the subnet or restarting the existing VMs within it, thus avoiding downtime. It's a direct and cost-effective way to increase the available IP address space within the existing subnet. This is a Google-recommended practice for handling subnet capacity issues.

D. Permitting additional traffic with firewall rules: Firewall rules control network traffic based on IP ranges, protocols, and ports. They do not increase the number of available private IP addresses within the subnet. This option does not address the core issue of IP address exhaustion.

Therefore, expanding the primary IP range of the existing subnet using the Google Cloud CLI is the recommended solution that meets all the requirements: addressing IP capacity, minimizing operational costs, and avoiding downtime.

Google Cloud Documentation References:

Expanding Subnet IP Ranges: https://cloud.google.com/vpc/docs/expand-subnet - This documentation explicitly describes how to expand the IP range of an existing subnet without downtime. It outlines the prerequisites and steps involved using the gcloud CLI or the Google Cloud Console.

VPC Network Overview: https://cloud.google.com/vpc/docs/vpc - Provides context on subnet IP ranges and their management.

===========

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.

===========

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

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

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

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

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/bigquery/docs/quickstarts/quickstart-web-ui?authuser=4

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.

Cloud Functions is Google Cloud’s event-driven serverless compute platform that lets you run your code locally or in the cloud without having to provision servers. Cloud Functions scales up or down, so you pay only for compute resources you use. Cloud Functions have excellent integration with Cloud Pub/Sub, lets you scale down to zero and is recommended by Google as the ideal serverless platform to use when dependent on Cloud Pub/Sub.

"If you’re building a simple API (a small set of functions to be accessed via HTTP or Cloud Pub/Sub), we recommend using Cloud Functions."

Ref: https://cloud.google.com/serverless-options

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

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

The Real description is about Coldline storage Class:

Coldline Storage

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

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

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

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

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

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."

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

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

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

As to mexblood1's point, CPU utilization is a recommended proxy for traffic when it comes to Cloud Spanner. See: Alerts for high CPU utilization The following table specifies our recommendations for maximum CPU usage for both single-region and multi-region instances. These numbers are to ensure that your instance has enough compute capacity to continue to serve your traffic in the event of the loss of an entire zone (for single-region instances) or an entire region (for multi-region instances). -https://cloud.google.com/spanner/docs/cpu-utilization

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.

To minimize latency for global users and support growth across multiple geographical regions, you must deploy your resources in those regions and use a global load balancing service.

Action B (Multi-Region Deployment): Deploying VMs in multiple regions closest to your users is necessary to reduce latency (by shortening the physical distance data must travel) and support regional growth.

Action C (Global External Application Load Balancer): The external Application Load Balancer (Global) is the Google-recommended Layer 7 solution for applications that require global reach. It provides a single anycast IP address and directs users to the nearest healthy backend, thus minimizing latency globally.

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

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

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

Example:

apiVersion: v1

kind: Pod

metadata:

name:my-gpu-pod

spec:

containers:

name:my-gpu-container

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

command: [/bin/bash]

resources:

limits:

nvidia.com/gpu: 2

nodeSelector:

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

gcloud 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 reason is that when you do health check, you want the VM to be working. Do the first check after initial setup time of 3 mins = 180 s < 200 s is reasonable.

The reason why our autoscaling is adding more instances than needed is that it checks 30 seconds after launching the instance and at this point, the instance isnt up and isnt ready to serve traffic. So our autoscaling policy starts another instance  again checks this after 30 seconds and the cycle repeats until it gets to the maximum instances or the instances launched earlier are healthy and start processing traffic  which happens after 180 seconds (3 minutes). This can be easily rectified by adjusting the initial delay to be higher than the time it takes for the instance to become available for processing traffic.So setting this to 200 ensures that it waits until the instance is up (around 180-second mark) and then starts forwarding traffic to this instance. Even after a cool out period, if the CPU utilization is still high, the autoscaler can again scale up but this scale-up is genuine and is based on the actual load.

Initial Delay Seconds  This setting delays autohealing from potentially prematurely recreating the instance if the instance is in the process of starting up. The initial delay timer starts when the currentAction of the instance is VERIFYING.

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

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

"Billing Account Viewer access would usually be granted to finance teams, it provides access to spend information, but does not confer the right to link or unlink projects or otherwise manage the properties of the billing account."https://cloud.google.com/billing/docs/how-to/billing-access

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

Google Cloud provides Cloud Audit Logs, which is an integral part of Cloud Logging. It consists of two log streams for each project: Admin Activity and Data Access, which are generated by Google Cloud services to help you answer the question of who did what, where, and when? within your Google Cloud projects.

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

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

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.

Preventing Accidental VM Deletion This document describes how to protect specific VM instances from deletion by setting the deletionProtection property on an Instance resource. To learn more about VM instances, read the Instances documentation. As part of your workload, there might be certain VM instances that are critical to running your application or services, such as an instance running a SQL server, a server used as a license manager, and so on. These VM instances might need to stay running indefinitely so you need a way to protect these VMs from being deleted. By setting the deletionProtection flag, a VM instance can be protected from accidental deletion. If a user attempts to delete a VM instance for which you have set the deletionProtection flag, the request fails. Only a user that has been granted a role with compute.instances.create permission can reset the flag to allow the resource to be deleted.https://cloud.google.com/compute/docs/instances/preventing-accidental-vm-deletion

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

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.

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

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

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.

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

"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/instances/start

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

Google HTTP(S) Load Balancing has native support for the WebSocket protocol when you use HTTP or HTTPS, not HTTP/2, as the protocol to the backend.

Ref: https://cloud.google.com/load-balancing/docs/https#websocket_proxy_support

So the next possible step is to Meet with the cloud enablement team to discuss load balancer options.

We dont need to convert WebSocket code to use HTTP streaming or Redesign the application, as WebSocket support is offered by Google HTTP(S) Load Balancing. Reviewing the encryption requirements is a good idea but it has nothing to do with WebSockets.

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

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/logging/docs/audit

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/billing/docs/how-to/export-data-bigquery "Cloud Billing export to BigQuery enables you to export detailed Google Cloud billing data (such as usage, cost estimates, and pricing data) automatically throughout the day to a BigQuery dataset that you specify."

Comprehensive and Detailed Explanation From Exact Extract:

The key requirements are a containerized web application that handles unpredictable traffic and must minimize costs.

Cloud Run is the ideal solution because it is a fully managed, serverless platform for containers that automatically scales to zero instances when there is no traffic. This directly fulfills the cost requirement by eliminating charges for idle resources. It excels at handling unpredictable, bursty traffic.

GKE Standard and Autopilot (Options B and D) incur costs for the cluster or nodes even when not serving traffic (unless carefully scaled down), making them less cost-efficient than Cloud Run's native scale-to-zero for unpredictable, non-constant workloads.

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

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.

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

The resource that you need to get access is in the other project.

roles/bigquery.dataViewer BigQuery Data Viewer

When applied to a table or view, this role provides permissions to:

Read data and metadata from the table or view.

This role cannot be applied to individual models or routines.

When applied to a dataset, this role provides permissions to:

Read the dataset's metadata and list tables in the dataset.

Read data and metadata from the dataset's tables.

When applied at the project or organization level, this role can also enumerate all datasets in the project. Additional roles, however, are necessary to allow the running of jobs.

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

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

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

Comprehensive and Detailed Explanation From Exact Extract:

The requirements point to the highest performance tier for a managed PostgreSQL-compatible database, specifically citing demands for transactional and analytical performance, synchronous replication, and storage optimization.

AlloyDB for PostgreSQL is Google Cloud's highly specialized, fully managed database that is optimized for demanding enterprise workloads. It is superior to standard Cloud SQL for performance and architectural features like synchronous replication and an optimized storage layer, directly matching all the stated requirements.

Database Migration Service (DMS) is the recommended tool for performing the migration itself.

Cloud Billing export to BigQuery enables you to export detailed Google Cloud billing data (such as usage, cost estimates, and pricing data) automatically throughout the day to a BigQuery dataset that you specify. Then you can access your Cloud Billing data from BigQuery for detailed analysis, or use a tool like Looker Studio to visualize your data.

The most secure method and Google-recommended practice for local testing of service account permissions is Service Account Impersonation, as it avoids the security risk of managing physical key files.

Option A (Impersonation): This method allows your user credentials to temporarily act as the service account. It is keyless, meaning no service account key needs to be created, downloaded, or stored, which is the cornerstone of secure credential management on Google Cloud.

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

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.

===========

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.

===========

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

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

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

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.

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

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

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

https://kubernetes.io/docs/tasks/debug-application-cluster/debug-application/#debugging-pods

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

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.

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

"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/concepts/overview

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

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

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.