Google Associate-Cloud-Engineer Dumps

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.

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.

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.

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

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.

cell phones are sending UDP packets and the only that can receive that type of traffic is a External Network

From this

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

"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

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

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

Signed URLs are used to give time-limited resource access to anyone in possession of the URL, regardless of whether they have a Google

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.

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.

"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."

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."

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."

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

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

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

You can deploy to a different project by using –project flag.

By default, the service is deployed the current project configured via:

$ gcloud config set core/project PROJECT

To override this value for a single deployment, use the –project flag:

$ gcloud app deploy ~/my_app/app.yaml –project=PROJECT

"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."

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: - 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: - Explains how to set up alerts based on metrics collected by Cloud Monitoring.

Dataflow Metrics: - Provides a comprehensive list of Dataflow metrics that can be used for monitoring and alerting.

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

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

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

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

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

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

Google Cloud Documentation References:

AlloyDB for PostgreSQL Overview: - This document highlights AlloyDB 's PostgreSQL compatibility, performance benefits, scalability, and suitability for migrating existing PostgreSQL workloads.

Spanner Overview: - This emphasizes Spanner 's unique features and differences from traditional relational databases like PostgreSQL.

Firebase Documentation: - This outlines the features of Firebase, including Firestore and Realtime Database, highlighting their NoSQL nature and incompatibility with PostgreSQL.

Cloud Bigtable Overview: - This describes Bigtable as a NoSQL database, emphasizing its differences from relational databases like PostgreSQL.

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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.

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.

C:\GCP\appeng>gcloud config list

[core]

account = xxx@gmail.com

disable_usage_reporting = False

project = my-first-demo-xxxx

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.

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

Pub/Sub documentation

Cloud Functions documentation

Choosing a messaging service for Google Cloud

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

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

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.

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

"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."

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

Two App engine can't be running on the same project: you can check this easy diagram for more

And you can't change location after setting it for your app

App Engine is regional and you cannot change an apps region after you set it. Therefore, the only way to have an app run in another region is by creating a new project and targeting the app engine to run in the required region (asia-northeast1 in our case).

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

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

gcloud services list

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

gcloud services list –available

--available

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

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

gcloud services list --enabled

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

gcloud services list –available

Cloud Run takes any container images and pairs great with the container ecosystem: Cloud Build, Artifact Registry, Docker. ... No infrastructure to manage: once deployed, Cloud Run manages your services so you can sleep well. Fast autoscaling. Cloud Run automatically scales up or down from zero to N depending on traffic.

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

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

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

Email/password

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

SAML

OIDC

Phone number

Custom

Anonymous

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

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

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

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

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

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

Managed instance groups documentation

Choosing a compute option for Google Cloud

/25:

CIDR to IP Range

Result

CIDR Range 172.16.20.128/25

Netmask 255.255.255.128

Wildcard Bits 0.0.0.127

First IP 172.16.20.128

First IP (Decimal) 2886734976

Last IP 172.16.20.255

Last IP (Decimal) 2886735103

Total Host 128

CIDR

172.16.20.128/25

/24:

CIDR to IP Range

Result

CIDR Range 172.16.20.128/24

Netmask 255.255.255.0

Wildcard Bits 0.0.0.255

First IP 172.16.20.0

First IP (Decimal) 2886734848

Last IP 172.16.20.255

Last IP (Decimal) 2886735103

Total Host 256

CIDR

172.16.20.128/24

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 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

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

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

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.

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.

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

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: - 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: - 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.

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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.

(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

Comprehensive and Detailed In Depth Explanation:

The core requirements are secure access to a specific BigQuery dataset from a Compute Engine instance, using short-lived credentials, adhering to Google's best practices, and minimizing operational overhead.

A. Project-level IAM role: Granting the BigQuery Data Viewer role at the project level gives the service account broad access to all BigQuery datasets within that project. This violates the principle of least privilege, a fundamental security best practice, as the application should only have access to the designated dataset.

B. Hourly new service account with dataset-level role: While this aims to achieve short-lived credentials, the operational burden of creating, attaching, and managing IAM policies for a new service account every hour is significant and not a Google-recommended practice for routine access. It introduces unnecessary complexity and potential for errors.

C. Custom service account with dataset-level IAM role: This is the recommended and most efficient approach. You create a dedicated Google Cloud service account specifically for this application. You then grant this service account the necessary IAM role (e.g., BigQuery Data Viewer, or a more specific custom role) directly on the target BigQuery dataset. When the Compute Engine instance runs as this service account, the Google Cloud client libraries automatically handle the acquisition and rotation of short-lived OAuth 2.0 access tokens from the instance's metadata server. This eliminates the need to manage long-lived credentials (like service account keys) and ensures the application only has access to the intended dataset. This adheres to the principle of least privilege and minimizes operational costs.

D. Hourly new service account with project-level role: This option combines the high operational overhead of frequently creating new service accounts with the security risk of granting overly permissive project-level access. It is not a recommended practice.

Therefore, the most secure, cost-effective, and operationally efficient solution is to create a custom service account, attach it to the Compute Engine instance, and grant it the appropriate BigQuery IAM role specifically on the target dataset. The platform handles the short-lived credentials automatically.

Google Cloud Documentation References:

Creating and enabling service accounts for instances: - Explains how to create and associate service accounts with Compute Engine VMs.  

Granting, changing, and revoking access to resources: - Details how to manage IAM policies and grant roles to service accounts on specific resources (like BigQuery datasets).  

BigQuery IAM roles: - Provides information on the various IAM roles available for controlling access to BigQuery resources at different levels (project, dataset, table, etc.).

Best practices for using service accounts: - Emphasizes the importance of the principle of least privilege and avoiding the management of long-lived service account keys when possible (relying on the metadata server for short-lived tokens).

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

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: - 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: - Provides context on subnet IP ranges and their management.

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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

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

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

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

Reference to Google Cloud Certified - Associate Cloud Engineer Documents:

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

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.

Comprehensive and Detailed In Depth Explanation:

The requirement is to export logs from Cloud Logging to a third-party SaaS tool with the least amount of delay possible. Let's analyze each option:

A. Cloud Scheduler, Cloud Function, and querying Cloud Logging: This approach introduces a delay based on the Cloud Scheduler's cron job frequency. The Cloud Function would periodically query Cloud Logging, which might not capture the logs in real-time. This does not meet the "least amount of delay possible" requirement.

B. Cloud Logging sink to Pub/Sub, SaaS tool subscribing to Pub/Sub: Cloud Logging sinks can be configured to export logs in near real-time as they are ingested into Cloud Logging. Pub/Sub is a messaging service designed for asynchronous and near real-time message delivery. By configuring the sink to send logs to a Pub/Sub topic, and having the SaaS tool subscribe to this topic, logs can be delivered to the SaaS tool with minimal delay. This aligns with the requirement for immediate export.

C. Cloud Logging sink to Cloud Storage, SaaS tool reading Cloud Storage: Exporting logs to Cloud Storage involves a batch-oriented approach. Logs are typically written to files periodically. The SaaS tool would then need to poll or be configured to read these files, introducing a significant delay compared to a streaming approach.

D. Cloud Logging sink to BigQuery, SaaS tool querying BigQuery: Similar to Cloud Storage, exporting to BigQuery is more suitable for analytical purposes. The SaaS tool would need to periodically queryBigQuery, which introduces latency and is not the most efficient way to achieve near real-time log delivery.

Therefore, configuring a Cloud Logging sink to Pub/Sub and having the SaaS tool subscribe to the Pub/Sub topic provides the lowest latency for exporting logs.

Google Cloud Documentation References:

Cloud Logging Sinks Overview: - This document explains how to create and manage Cloud Logging sinks, including the available destinations.

Pub/Sub Overview: - This highlights Pub/Sub 's capabilities for real-time message delivery and its use cases in streaming data.

Exporting Logs with Cloud Logging: - This provides a comprehensive guide to exporting logs from Cloud Logging to various destinations, emphasizing Pub/Sub for streaming.

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

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.

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

Instance groups are collections of virtual machine (VM) instances that you can manage as a single entity. Instance groups can help you simplify the management of multiple instances, reduce operational costs, and improve the availability and performance of your applications. Instance groups support autoscaling, which automatically adds or removes instances from the group based on increases or decreases in load. Autoscaling helps your applications gracefully handle increases in traffic and reduces cost when the need for resources is lower. You can set the autoscaling policy based on CPU utilization, load balancing capacity, Cloud Monitoring metrics, or a queue-based workload. In this case, since the video encoding software is CPU-intensive, setting the autoscaling based on CPU utilization is the best option to ensure high availability and optimal performance. References:

Instance groups

Autoscaling groups of instances

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

Let's analyze each option:

A. Using gsutil: While gsutil can transfer data to Cloud Storage, for 150 TB of infrequently accessed data, direct transfer over the network might be slow and consume significant bandwidth, potentially impacting other network operations. It also lacks built-in mechanisms for filtering files based on age.

B. Using Cloud Interconnect and Filestore: Cloud Interconnect provides a dedicated connection, but Filestore is a fully managed NFS service primarily designed for high-performance file sharing for applications running in Google Cloud. Migrating 150 TB of infrequently accessed data to Filestore would be cost-inefficient compared to Cloud Storage and doesn't directly address the requirement of moving older than one year files.

C. Using Transfer Appliance: Transfer Appliance is suitable for very large datasets (petabytes) or when network connectivity is poor or unreliable. While it addresses bandwidth concerns, it involves a physical appliance and might be an overkill for 150 TB of data, especially if network connectivity is reasonable.

D. Using Storage Transfer Service: Storage Transfer Service is specifically designed for moving large amounts of data between online storage systems, including on-premises file systems and Cloud Storage. It offers features like filtering by file age, scheduling transfers, and bandwidth control, directly addressing all the requirements of the question: migrating infrequently accessed files older than one year to Cloud Storage, minimizing costs (by using appropriate Cloud Storage classes for infrequent access), and controlling bandwidth usage.

Google Cloud Documentation References:

Storage Transfer Service Overview: - This page details the capabilities and use cases of Storage Transfer Service, including transferring from on-premises.

Storage Transfer Service for on-premises data: - This specifically covers transferring data from on-premises file systems.

Cloud Storage Classes: - Understanding the different storage classes (Standard, Nearline, Coldline, Archive) is crucial for cost optimization of infrequently accessed data. Storage Transfer Service can be configured to move data to a cost-effective class like Nearline or Coldline.

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Understanding IAM custom roles

Key Point: Custom roles enable you to enforce the principle of least privilege, ensuring that the user and service accounts in your organization have only the permissions essential to performing their intended functions.

Basic concepts

Custom roles are user-defined, and allow you to bundle one or more supported permissions to meet your specific needs. Custom roles are not maintained by Google; when new permissions, features, or services are added to Google Cloud, your custom roles will not be updated automatically.

When you create a custom role, you must choose an organization or project to create it in. You can then grant the custom role on the organization or project, as well as any resources within that organization or project.

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

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

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

The 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.