Here are the top 30 Google Cloud Machine Learning Engine interview questions with their answers:

1. What is Google Cloud Machine Learning Engine?

Ans: Google Cloud Machine Learning Engine is a managed service provided by Google Cloud Platform (GCP) that allows you to build and deploy machine learning models at scale.

2. How does Google Cloud Machine Learning Engine differ from other machine learning platforms?

Google Cloud Machine Learning Engine provides a fully managed infrastructure that takes care of provisioning, scaling, and managing resources for training and deploying machine learning models. It integrates seamlessly with other GCP services, making it easy to build end-to-end machine-learning pipelines.

3. What are the key components of the Google Cloud Machine Learning Engine?

Ans: The key components of Google Cloud Machine Learning Engine are:

• Model: The trained machine learning model.
• Training job: The process of training a model using training data.
• Online prediction: The ability to serve predictions in real time.
• Batch prediction: The ability to process predictions on large datasets.

4. How can you train a machine learning model using Google Cloud Machine Learning Engine?

Ans: You can train a machine learning model using Google Cloud Machine Learning Engine by defining a training job configuration that includes details such as the training data, model architecture, hyperparameters, and resource requirements. The training job is submitted to the service, which handles the provisioning and management of resources.

5. What is the role of TensorFlow in the Google Cloud Machine Learning Engine?

Ans: TensorFlow is an open-source machine learning framework developed by Google. It is widely used in Google Cloud Machine Learning Engine for building and training machine learning models. TensorFlow provides a high-level API that simplifies the process of defining, training, and serving models.

6. How can you deploy a trained model on Google Cloud Machine Learning Engine?

Ans: To deploy a trained model on Google Cloud Machine Learning Engine, you need to create a model resource and specify the location of the model files. The model can be deployed as a version, which represents a specific instance of the model. Once deployed, you can use the model to make predictions.

7. How does Google Cloud Machine Learning Engine handle model versioning?

Ans: Google Cloud Machine Learning Engine allows you to create multiple versions of a model. This enables you to deploy and test different versions of the model simultaneously. You can also promote a version to make it the default version for serving predictions.

8. How can you monitor and evaluate the performance of a deployed model?

Ans: Google Cloud Machine Learning Engine provides monitoring capabilities to track the performance of deployed models. You can monitor metrics such as prediction latency, error rates, and resource utilization. Additionally, you can use the Cloud Monitoring service to set up custom monitoring dashboards.

9. What is hyperparameter tuning, and how can you perform it using Google Cloud Machine Learning Engine?

Ans: Hyperparameter tuning involves finding the optimal values for the hyperparameters of a machine-learning model. Google Cloud Machine Learning Engine provides a hyperparameter tuning service called Cloud Hyperparameter Tuning. It automates the process of searching for the best hyperparameter configuration by running multiple training jobs with different hyperparameter values.

10. How does Google Cloud Machine Learning Engine handle scalability and resource management?

Ans: Google Cloud Machine Learning Engine automatically scales the underlying infrastructure based on the resource requirements of training and prediction tasks. It allows you to specify the amount of resources required for training jobs and handles the provisioning and scaling of virtual machines.

11. Describe the various layers of cloud architecture.

Ans: The various layers of cloud architecture are as follows:

• Physical Layer: This layer comprises physical servers, networks, and other things that can be managed and controlled in the real world.
• Platform Layer: This layer contains services such as the OS and applications. It serves as a development and deployment platform.
• Infrastructure Layer: This covers networking, virtualized servers, and storage resources.
• Application Layer: End-users interact directly with the Application Layer. This layer is configurable and scalable. Metadata lets customers customize the software.

12. What does “EUCALYPTUS” mean specifically in the context of cloud computing?

” EUCALYPTUS ” is an open-source cloud computing infrastructure. The full form of EUCALYPTUS refers to “Elastic Utility Computing Architecture.” EUCALYPTUS allows developers to quickly and easily create private, public, and hybrid cloud environments. You can take advantage of the cloud and all it offers by establishing your own data center in the cloud.

13. What do you mean by Google Compute Engine?

Ans: The Google Cloud Platform relies on the Google Cloud Engine as its backbone. This Google-hosted IaaS allows users to run their own Windows or Linux virtual machines. Long-term storage and KVM make it possible for virtual machines to function.

14. What are the various methods for authenticating the Google Compute Engine API?

Ans: Google Compute Engine API authentication can be done in different ways:

• Through client library
• Using OAuth 2.0
• Conveniently with an entrance token.

15. What are the most used open-source cloud computing platforms?

• Apache Mesos
• OpenStack
• Cloud Foundry
• KVM.

16. What do you know about Google Compute Engine?

Ans: Google Cloud Engine is the basic component of the Google Cloud Platform. So, it becomes a common question that lies under the Google Cloud Engineer interview questions as well as Google Cloud Architect interview questions.

Google Compute Engine is an IaaS product that offers self-managed and flexible virtual machines that are hosted on the infrastructure of Google. It includes Windows and Linux-based virtual machines that may run on local, KVM, and durable storage options.

It also includes REST-based API for control and configuration purposes. Google Compute Engine integrates with GCP technologies such as Google App Engine, Google Cloud Storage, and Google BigQuery in order to extend its computational ability and thus create more sophisticated and complex applications.

17. How are the Google Compute Engine and Google App Engine related?

Ans: This typical and straightforward question is a part of the frequently asked Google Cloud Platform interview questions and answers, and can be answered like this. Google Compute Engine and Google App Engine are complementary to each other. Google Compute Engine is the IaaS product whereas Google App Engine is a PaaS product of Google.

Google App Engine is generally used to run web-based applications, mobile backends, and line of business. If you want to keep the underlying infrastructure in more of your control, then Compute Engine is a perfect choice. For instance, you can use Compute Engine for the implementation of customized business logic or in case, you need to run your own storage system.

18. How does the pricing model work in GCP cloud?

Ans: While working on the Google Cloud Platform, the user is charged on the basis of compute instance, network use, and storage by Google Compute Engine. Google Cloud charges virtual machines on the basis of per second with the limit of minimum of 1 minute. Then, the cost of storage is charged on the basis of the amount of data that you store.

The cost of the network is calculated as per the amount of data that has been transferred between the virtual machine instances communicating with each other over the network. You should prepare yourself with the questions on Google Cloud Platform pricing models as these are among the most common Google Cloud interview questions.

19. What are the different methods for the authentication of Google Compute Engine API?

Ans: This is one of the popular Google Cloud architect interview questions which can be answered as follows. There are different methods for the authentication of Google Compute Engine API:

Using OAuth 2.0
Through client library
Directly with an access token

20. What are the service accounts? How will you create one?

Ans: This is one of the most common Google Cloud interview questions and the detailed answer to it can be given this way. The special accounts related to a project are known as the Service Accounts. The service accounts are used for the authorization of Google Compute Engine so that it could perform on behalf of the user and thus could access non-sensitive data and information.

21. Why do we require the virtualization platform for cloud implementation?

Virtualization makes it possible to create operating systems, virtual versions of storage, networks, applications, etc. With the right virtualization, we can increase the existing infrastructure. Multiple applications and operating systems can be executed on existing servers.

22. How does Elasticity differ from scalability?

Ans: Scalability is a cloud computing feature that enables it to adapt to growing workloads by scaling up the capacity of resources. The architecture utilizes scalability to provide on-demand resources when traffic expands the need. Meanwhile, Elasticity is a property which enables the dynamic commissioning and dismantling of huge quantities of resources. It is based on the level of availability of resources and how long they are used.

23. What is the relationship between Google Compute Engine and Google App Engine?

Ans: Google App Engine and Google Compute Engine benefit one another. Google Application Engine is a PaaS service, whereas GCE is an IaaS service. GAE is widely used to power mobile backends, web-based apps, and line-of-business applications. Computs Engine is a great option if we need more control on the underlying infrastructure. Compute Engine, for instance, can be used to build a customized business logic or execute our own storage system.

24. What is EUCALYPTUS?

Ans: EUCALYPTUS stands for “Elastic Utility Computing Architecture For Linking Your Program To Useful Systems”. This is a free architecture for cloud computing software which is employed to build cloud computing clusters. It provides private, public, and hybrid cloud services.

25. What are the various ways to authenticate the Google Compute Engine API?

Ans: Different methods are available for Google Compute Engine API authentication:

• Using OAuth 2.0
• Through client library
• Directly with an access token

26. What do you think “Managed VMs” means when it comes to GCP?

Ans: Google managed VMs, or virtual machines, in this context. Google takes care of the infrastructure, including the host operating system, virtualization layer, and hardware, when you launch a virtual machine on GCP using Managed VMs. It can simplify your workflow and allow you to concentrate on developing and deploying applications.

27. What’s the difference between PaaS and IaaS?

Ans: IaaS: IaaS, which stands for “Infrastructure as a Service,” is a type of cloud computing that lets users access a virtualized computing environment. This can include storage, networking, and servers, among other things.
Paas: PaaS, which stands for “Platform as a Service.” is a type of cloud computing that gives users access to a platform for testing, building, and deploying applications. PaaS can make it easier to build and deploy apps because it takes care of much of the infrastructure behind them.

28. What is autoscaling in GCP?

Ans: Auto-scaling is possible with the Google Cloud Platform’s managed instance groups. Managed instance groups are collections of identical instances that were created from the same master template. The easiest way to auto-scale in Avi Vantage is to scale based on how much CPU a group of virtual machine instances uses.

29. What is the use of a bucket in Google Cloud Storage?

Ans: Buckets are simple containers that are used to store data. Everything you put in Cloud Storage must be in a “bucket.” There is no limit on how many buckets you can make or delete. But buckets can’t be put inside each other as directories and files can.

30. What is meant by Google Cloud APIs?

Ans: Google Cloud APIs are most useful when used to automate processes in a language of your choosing. With the help of APIs, multiple Google services can talk to one another and be incorporated into third-party applications. Another way to think of it is as an intermediary through which end users can access cloud-based resources and applications.