Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. It is a public cloud computing platform consisting of a variety of services like compute, storage, networking, application development, Big Data, and more, which run on the same cloud infrastructure that Google uses internally for its end-user products, such as Google Search, Photos, Gmail and YouTube, etc.

The services of GCP can be accessed by software developers, cloud administrators and IT professionals over the Internet or through a dedicated network connection.

Google Cloud Platform is known as one of the leading cloud providers in the IT field. The services and features can be easily accessed and used by the software developers and users with little technical knowledge. Google has been on top amongst its competitors, offering the highly scalable and most reliable platform for building, testing and deploying the applications in the real-time environment.

Apart from this, GCP was announced as the leading cloud platform in the Gartner’s IaaS Magic Quadrant in 2018. Gartner is one of the leading research and advisory company. Gartner organized a campaign where Google Cloud Platform was compared with other cloud providers, and GCP was selected as one of the top three providers in the market.

Explore a comprehensive set of Google Cloud Platform interview questions and expert answers to help you navigate your next interview with confidence. Gain insights into key GCP concepts, architecture, services, and best practices from seasoned professionals in the field.

Experienced Google Cloud Platform Interview Questions

Discs and other forms of data storage have become outdated as a result of the development and proliferation of cloud computing over the past few years, which is the answer. 

Users may now easily upload files of any sort to a cloud storage service, which will keep their data safe and make it accessible even after a significant amount of time has passed. When something is uploaded, it will be preserved indefinitely, until the user deletes the individual item or the file itself. Even if this is a general problem with cloud computing, you might be able to discover a solution to it by looking through the questions and answers provided in the Google Cloud interview.

The answer is that cloud computing was built so that its clients can access their data whenever and wherever they need it. This was the primary motivation behind its development. As a result of developments in technology and the accessibility of services such as Google Cloud, the concept may now be realized with a great deal less difficult than it was before possible.

Users have the ability to access their data from any location, at any time, via any device, and at their own convenience thanks to Google Cloud.

This is an example of a Google Cloud Platform interview question and answer that is considered to be one of the most fundamental. The following is a condensed version of the information that was used to answer this question.

Google has developed a platform called Google Cloud Platform specifically for those who are interested in capitalizing on the various benefits that come with cloud computing. Google Cloud Platform (GCP) is a platform that offers a wide variety of services in the field of cloud computing. These services include compute, database, storage, migration, and networking.

Users who use Google Compute Engine are charged for the amount of time they spend using Google Cloud Platform based on the amount of storage space, network traffic, and compute instances they consume. The cost of running a virtual machine on Google Cloud is calculated on a per-second basis, with a minimum charge of one minute. Your storage price will ultimately be determined by the total amount of data you have in your account.

The total amount of money spent on the network is directly proportional to the total amount of data that was exchanged between the virtual machines (VMs) that were interacting with one another. You should familiarize yourself with the various price structures utilized by Google before going in for an interview with Google Cloud Platform if you want to do well.

The project identifier and the project number are the two components that can be utilized to generate a one-of-a-kind identifier for a certain endeavour. It is possible to differentiate between them both by –

In contrast to the user-generated project number, the project number is automatically produced whenever a new project is formed. The user is responsible for producing the project number. Although the project id is not required for many of our services, we do require the project number (but it is a must for the Google Compute Engine).

In the event that you are interviewing to become a Google Cloud Engineer, this is an excellent illustration of a question that is straightforward yet has the potential to be significant. Therefore, it is absolutely necessary to go through the fundamentals of projects before heading for the interview with Google Cloud.

Every Google Compute Engine project has a default allocation of resources that is assigned to it. There is also the possibility of increasing quotas on a project-by-project basis. On the quota tab of the Google Cloud Platform Console, one is able to observe the various limits that are currently in place for the project.

If you discover that the quota limit for your account has been reached and you would like to make a request for more resources, you can do so through the quotas page found in the IAM. You can quickly and easily ask for extra allocation by clicking on the Edit Quotas link that is located in the top right corner of the page.

These Google Cloud interview questions might be asked of you during an interview for the Google Cloud Architect position or the Google Cloud Consultant position. You need to put in a lot of effort studying if you want to do well in the interview.

The response is deceptively basic, although it does require an in-depth understanding of the cloud infrastructure of Google. The answer provided here is an effective response to one of the most challenging Google Cloud Platform interview questions.

When an instance is deleted, there is no way to retrieve it again once it has been removed. Restarting it will bring it back to life if it was paused at any point during the process.

In the Google Cloud dashboard, a single project can be associated with many instances, and each instance can be associated with a different number of projects. When creating instances for a project, you have the option of using a diverse selection of operating systems and hardware architectures.

When you delete an instance, it is removed from the project entirely and never returns. Each instance of Compute Engine comes pre-configured with a small boot persistent CD on which the operating system is pre-installed. This is a standard feature. You have the option of adding more storage options to your instance if the data storage needs of your applications require more capacity than you currently have available.

The answer to this question is that the Google Cloud Platform already has the capability to save one-of-a-kind photos thanks to the applications that are preinstalled on the platform. Machine Images, a brand-new feature that is now in beta testing, contain all of the setup parameters, including permissions, in contrast to a custom image, which is merely an image of a disc. There may be more than one disc in machine photographs.

Utilizing pictures of different types of machinery can help you accomplish two different objectives. There is a second one available in case the first one is damaged. With the differential disc backup characteristics of machine images, a VM snapshot can be saved while using up less space on the disc and operating more effectively. This is made possible by the machine images.

It is also possible to use it as a model for the creation of new virtual machines (VMs). By making use of an override, the image’s characteristics can be customized in a unique manner for each copy.

The correct response is that normal preemptible VM instances are anywhere from 60-91% less expensive than standard VMs. On the other hand, Compute Engine may choose to shut down (also known as “preempt”) particular VMs in order to free up additional resources for use by other VMs. It is not always possible to access preemptible instances because doing so requires additional resources from Google Compute Engine.

Preemptible virtual machines (VMs) need a specific amount of CPU time in order to execute, just like conventional virtual machines need. You can consider requesting a separate “Preemptible CPU” allocation in order to prevent your preemptible virtual machines (VMs) from consuming too much of the CPU allotment that is reserved for your regular VMs.

While a Compute Engine standard CPU quota continues to be in force for all standard virtual machines in a particular region, a Compute Engine preemptible CPU quota applies to all preemptible virtual machines in that region.

It is possible to use the standard CPU quota instead of the preemptible CPU quota when installing preemptible virtual machines onto a host that does not have a preemptible CPU quota. Additionally, you will require some usual extras, such as Internet Protocol (IP) and storage space. Only once Compute Engine has assigned a limit will it appear in the gcloud CLI or Cloud console quota pages as a preemptible CPU limit. This is the case regardless of whether you use the console or the CLI.

In response to your inquiry, I can tell you that autoscaling is a feature that is available on the Google Cloud Platform within the controlled instance groups. A “managed instance group” is a collection of linked instances that have been derived from the same master template. These instances have been grouped together for management purposes. Please refer to the article on Instance Groups for any additional information regarding managed instance groups. One of the many ways that autoscaling can be accomplished with Avi Vantage is by adjusting the number of active virtual machines to correspond with the amount of processing power that is required by each individual machine. This is the simplest method.

It is possible to construct auto-scale groups for either a single zone or numerous zones (regional). The availability of your application’s instances can be increased for users by spreading them out across a number of different zones. A managed instance group that serves a region will not generate instances in more than those three availability zones, even if the area contains more than three availability zones. When creating instances, you are not restricted to the use of simply the three-zone setup; instead, you have the option of using either the two-zone setup or the multi-zone setup.

As a result of this, Vertex AI consolidates AutoML and AI Platform into a cohesive collection of application programming interfaces (APIs), client libraries, and user interfaces. Vertex AI provides users with access to AutoML as well as customizable training methods. After training your models in any way that you see appropriate, Vertex AI grants you the ability to save, deploy, and request predictions from those models. It is possible to speed up the process of developing, deploying, and scaling machine learning models by utilizing pre-trained tools and bespoke tools on a single AI platform.

It is feasible to migrate or upgrade programs and process data on-premises by utilizing a number of Google Cloud services, such as databases, machine learning, data analytics, and container management services. It is doable to make use of services provided by a third party. Any one of these four locations—network Google’s edge, an Operator data center, a Customer data center, or a Client data center—is capable of hosting the operation of Google Distributed Cloud products. The Google Distributed Cloud products can be run from any one of these four locations, making them all viable options.

The shift to cloud computing is becoming increasingly necessary for businesses of all sizes. They are looking for a means to increase productivity while simultaneously lowering risk and accelerating the rate of innovation in their organization. Certain workloads cannot be moved instantly or completely to the public cloud because of factors such as compliance and data sovereignty requirements, low latency or local data processing needs, and the demand for services that are close together or nearby. Other factors include the demand for services that are close together or nearby.

Google introduced Google Distributed Cloud at Google Cloud Next ’21. This is a collection of hardware and software solutions that extends Google’s infrastructure to the edge and into your data centers while guaranteeing that these workloads can still make use of the cloud’s resources.

In the years that have passed since its initial release, Google’s Kubernetes has established itself as the industry standard for container orchestration within businesses. Google Kubernetes Engine is utilized by businesses that place the utmost importance on their applications to maintain the greatest levels of dependability, security, and scalability (GKE).

In the second quarter of 2020, more than one hundred thousand companies all around the world utilized at least one of our application modernization platforms or services, such as GKE. Up until quite recently, optimizing Kubernetes typically required a significant amount of manual configuration. You can now focus on your programme without having to worry about the underlying infrastructure now that GKE Autopilot, the new mode of operation for managed Kubernetes, is ready.

Kubernetes and Google Container Engine (GKE) are wonderful options for many companies since they offer powerful and versatile cluster management along with complete administrative access. For some people, the level of control and flexibility may be excessive or daunting in relation to the amount of work they have to do, while for others, it may represent a straightforward method for generating a more secure and consistent atmosphere in which to create.

Because it maintains the cluster’s architecture, control plane, and nodes, the autopilot may make it possible for businesses to install Kubernetes and streamline operations. This is because Kubernetes is a container orchestration system.

Students will learn how to construct containerized applications and deploy them using Google Kubernetes Engine by taking this course (GKE). Participants investigate and install different components of the solution, such as infrastructure pieces like pods and containers, through a combination of talks, live demos, and hands-on laboratories.

The Binary Authorization is utilized by both Google Kubernetes Engine (GKE) and Cloud Run to verify that only legitimate container images are deployed. This is done to prevent any errors from occurring. You may ensure that only photographs that have been signed by reputable authorities were used in production by utilizing Binary Authorization, which enables you to enforce signature validation during the deployment phase. 

You can have peace of mind knowing that only validated images are utilized in the build and release process if you validate your images before beginning those processes. You will have a greater degree of command over your containerized infrastructure as a result of this.

When it comes to cloud services, scalability, adaptability, and cost-effectiveness are three of the most important factors to consider.

One of the most important features of a cloud service is the ability to raise or decrease consumption without incurring any additional costs. This is one of the most valuable qualities of a cloud service. This is a significant advantage in comparison to more conventional, on-premise systems, the expansion of which might often be financially impossible.

The versatility of cloud services is another thing that sets them apart. A cloud service provides businesses with a great deal of versatility in terms of pricing and service levels, giving them the ability to find the arrangement that is best suited to their particular necessities.

The inexpensive cost of cloud services is another significant advantage of using these services. Because of the lower overhead costs involved with their mode of distribution, online services are frequently more cost-effective than on-premise ones. It may be possible to acquire them on a “pay as you go” basis, which could result in additional savings on costs.

It’s possible that the cloud is made up of a lot of different and complicated elements. A system integrator in the cloud is required for a variety of cloud-related tasks, including the development of a cloud, the integration of its numerous components, and the establishment of a hybrid or private cloud network.

Each customer in a single multi-tenant SaaS environment has their own dedicated set of resources, so they do not need to worry about sharing them with other tenants.

A more nuanced approach to multi-tenancy: The same collection of features is made accessible to multiple tenants through the utilization of a SaaS deployment strategy that pools the resources at their disposal.

Using virtualization technology, it is possible to generate a variety of different things, including operating systems, virtual storage, networks, applications, and so on. Utilizing virtualization will allow for the expansion of the currently installed infrastructure. Many applications and operating systems are compatible with the servers that are now available.

Sadly, this is a typical question asked at interviews for Google Cloud jobs. The progress of project-specific services can be monitored through the use of service accounts. They are utilized in order to grant permission to Google Compute Engine to act on the user’s behalf, hence providing the service access to data that is considered to be relatively harmless.

The Google Cloud Platform Console and the Google Compute Engine service accounts are the most often used of the many different kinds of service accounts that Google offers.

It is not necessary for the user to create an account for the service on their own. This file is automatically generated by the Compute Engine whenever a new instance of something is created. When an instance is created in Google Compute Engine, an administrator has the ability to restrict the privileges of the service account that is connected with the instance.

This is one of the most common Google Cloud interview questions. Service accounts are the special accounts related to a project. They are used for the authorization of Google Compute Engine in order to be able to perform on behalf of the user thus receiving access to non-sensitive data.

There are different service accounts offered by Google but mainly, users prefer to use Google Cloud Platform Console and Google Compute Engine service accounts.

The user doesn’t need to create a service account manually. It is automatically created by the Compute Engine whenever a new instance is created. Google Compute Engine also specifies the scope of the service account for that particular instance when it is created.

Projects are the containers that organize all the Google Compute resources. They comprise the world of compartments and are not meant for resource sharing. Projects may have different users and owners.

While It is a very simple question, it is based on a deep understanding of the Google cloud platform. The answer is no. It is not possible to retrieve the instances that have been deleted once. However, if it has been stopped, it can be retrieved by simply starting it again.

Google BigQuery is used as a data warehouse and stores all the analytical data in an organization. It organizes the data table into datasets.

Some of the benefits of BigQuery for the data warehouse practitioners are:

  • BigQuery allocates query and storage resources depending on the requirement and usage. Therefore, it doesn’t require the provisioning of resources before usage.
  • It can store data in different formats for efficient storage management. For example, Google’s distributed file system, proprietary format, proprietary columnar format, query access pattern, etc.
  • It is fully maintained and managed without any downtime or hindrance.
  • It provides backup and disaster recovery at a broader level. Users can easily undo changes and revert to a previous state without making a request for the backup recovery.

Google Cloud SDK (Software Development Kit) is a set of tools that are used in the management of applications and resources that are hosted on the Google Cloud Platform. It is comprised of the gcloud, gsutil, and bqcommand line tools.
Google Cloud SDK runs only on specific platforms like Windows, Linux, and macOS and requires Python 2.7.x. Other specific tools in the kit may have additional requirements as well.

Google Cloud APIs are programmatic interfaces  that allow users to add the power of everything (from storage access to the image analysis based on machine learning) to Google Cloud-based applications.

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