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Google Professional-Cloud-Developer certification is an excellent way for developers to demonstrate their skills and knowledge in developing and deploying applications on Google Cloud. It is a highly respected certification in the industry, and it can help individuals advance their careers and increase their earning potential.

The Google Certified Professional - Cloud Developer certification exam covers a wide range of topics related to developing applications on GCP. These include deploying applications, managing application performance, working with data storage services, and integrating Google Cloud services. Professional-Cloud-Developer Exam also covers topics such as testing and debugging applications, designing and developing highly scalable and resilient applications, and managing application security.

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Being a Google-certified professional developer is a significant achievement that can open up new job opportunities and make one an invaluable asset to an organization. It also showcases a developer's expertise and skillset, improving their credibility and knowledge in the eyes of employers and partners. In conclusion, Google's Professional-Cloud-Developer Exam is an excellent opportunity for developers to earn a certification that validates their expertise in cloud computing, helping them to remain relevant and competitive in today's fast-paced tech industry.

Google Certified Professional - Cloud Developer Sample Questions (Q35-Q40):

NEW QUESTION # 35
For this question, refer to the HipLocal case study.
HipLocal's application uses Cloud Client Libraries to interact with Google Cloud. HipLocal needs to configure authentication and authorization in the Cloud Client Libraries to implement least privileged access for the application. What should they do?

• A. Create a service account for the application. Export and deploy the private key for the application. Use the service account to interact with Google Cloud.
• B. Create a service account for the application and for each Google Cloud API used by the application.
  Export and deploy the private keys used by the application. Use the service account with one Google Cloud API to interact with Google Cloud.
• C. Use the default compute service account to interact with Google Cloud.
• D. Create an API key. Use the API key to interact with Google Cloud.

Answer: D

 

NEW QUESTION # 36
Your team develops services that run on Google Cloud. You need to build a data processing service and will use Cloud Functions. The data to be processed by the function is sensitive. You need to ensure that invocations can only happen from authorized services and follow Google-recommended best practices for securing functions. What should you do?

• A. Create a service account with the Cloud Functions Viewer role. Use that service account to invoke the function.
• B. Create an OAuth 2.0 client ID for your calling service in the same project as the function you want to secure. Use those credentials to invoke the function.
• C. Create a service account with the Cloud Functions Invoker role. Use that service account to invoke the function.
• D. Enable Identity-Aware Proxy in your project. Secure function access using its permissions.

Answer: C

Explanation:
Reference:
https://medium.com/google-cloud/how-to-securely-invoke-a-cloud-function-from-google-kubernetes-engine-run

 

NEW QUESTION # 37
Your application requires service accounts to be authenticated to GCP products via credentials stored on its host Compute Engine virtual machine instances. You want to distribute these credentials to the host instances as securely as possible. What should you do?

• A. Generate a P12 file from the GCP Console after the instance is deployed, and copy the credentials to the host instance before starting the application.
• B. Use the instance's service account Application Default Credentials to authenticate to the required resources.
• C. Use HTTP signed URLs to securely provide access to the required resources.
• D. Commit the credential JSON file into your application's source repository, and have your CI/CD process package it with the software that is deployed to the instance.

Answer: B

Explanation:
Reference: https://cloud.google.com/compute/docs/api/how-tos/authorization

 

NEW QUESTION # 38
You are developing an application using different microservices that should remain internal to the cluster. You want to be able to configure each microservice with a specific number of replicas. You also want to be able to address a specific microservice from any other microservice in a uniform way, regardless of the number of replicas the microservice scales to. You need to implement this solution on Google Kubernetes Engine. What should you do?

• A. Deploy each microservice as a Pod. Expose the Pod in the cluster using an Ingress, and use the Ingress IP address name to address the Pod from other microservices within the cluster.
• B. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using a Service, and use the Service DNS name to address it from other microservices within the cluster.
• C. Deploy each microservice as a Pod. Expose the Pod in the cluster using a Service, and use the Service DNS name to address the microservice from other microservices within the cluster.
• D. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using an Ingress, and use the Ingress IP address to address the Deployment from other microservices within the cluster.

Answer: B

 

NEW QUESTION # 39
Your company's product team has a new requirement based on customer demand to autoscale your stateless and distributed service running in a Google Kubernetes Engine (GKE) duster. You want to find a solution that minimizes changes because this feature will go live in two weeks. What should you do?

• A. Deploy a Vertical Pod Autoscaler, and scale based on a custom metric.
• B. Deploy a Horizontal Pod Autoscaler, and scale based on a custom metric.
• C. Deploy a Horizontal Pod Autoscaler, and scale based on the CPU toad.
• D. Deploy a Vertical Pod Autoscaler, and scale based on the CPU load.

Answer: C

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/horizontalpodautoscaler The Horizontal Pod Autoscaler changes the shape of your Kubernetes workload by automatically increasing or decreasing the number of Pods in response to the workload's CPU or memory consumption, or in response to custom metrics reported from within Kubernetes or external metrics from sources outside of your cluster.

 

NEW QUESTION # 40
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