Google Professional-Cloud-Developer : Google Certified Professional - Cloud Developer
- Exam Code: Professional-Cloud-Developer
- Exam Name: Google Certified Professional - Cloud Developer
- Updated: Nov 11, 2026
- Q & A: 400 Questions and Answers
About Google Professional-Cloud-Developer Exam
Section 3: Deploying Apps
This topic evaluates the ability of the students to recommend the relevant deployment strategies with the help of the appropriate tools, such as Spinnaker, Cloud Build, Anthos Configuration Manager, and Tekton, for a certain compute environment (for instance, Google Kubernetes Engine, Compute Engine, etc.). The individuals should also possess the skills in deploying apps & services on Compute Engine; deploying apps & services to Google Kubernetes Engine (GKE); deploying Cloud Function. Lastly, they must have the proficiency in utilizing service accounts.
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Building & Testing Apps
- Set up a Local Development Environment: This subsection measures your skills in creating different Google Cloud projects, using developing tools and command-line interfaces, Cloud Shell tools, and Google Cloud Console, as well as imitating Google Cloud services for the development of the local application.
- Testing: Here, an applicant needs to have the skills in unit testing, performance testing, integration testing, and load testing.
- Building: It covers the skills in managing source control, improving and reviewing continuous efficiency in the integration pipeline, as well as creating different secure container images through code.
- Write Efficient Code: The potential candidates should understand algorithm design, software development methodologies, modern app patterns, and debugging & profiling code.
Reference: https://cloud.google.com/certification/cloud-developer
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Aside from the mentioned training options, Google also provides a series of programs that will allow you to demonstrate competence in the following areas:
- Building and Managing APIs with Apigee
- Serverless Firebase Development
- Deploying to Kubernetes in Google Cloud
- Serverless Cloud Run Development
Through all the above-mentioned courses, exam-takers will obtain fundamental information relating to Cloud Shell, Cloud IAM, Container Registry, Spring Boot, App Engine, Firebase SDK, and many other tools and products. It should be mentioned that these courses have a total of 64 labs that will be helpful in validating your skills to manage the Google cloud environment in real-world scenarios.
Google Professional-Cloud-Developer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Designing cloud-native applications | - Application architecture design - Microservices design principles - Scalability and resiliency patterns |
| Topic 2: Ensuring application performance and reliability | - Monitoring and logging (Cloud Monitoring, Cloud Logging) - Performance optimization - Fault tolerance and incident response |
| Topic 3: Integrating Google Cloud services | - Using managed services (Pub/Sub, Firestore, Cloud SQL) - API integration and service-to-service communication |
| Topic 4: Building and testing applications | - Debugging and troubleshooting applications - Unit and integration testing strategies - Application development on Google Cloud |
| Topic 5: Deploying applications | - CI/CD pipelines - Containerization and deployment (Cloud Run, GKE) - Release management strategies |
Google Certified Professional - Cloud Developer Sample Questions:
You are developing a new API that creates requests on an asynchronous message service.
Requests will be consumed by different services. You need to expose the API by using a gRPC interface while minimizing infrastructure management overhead. How should you deploy the API?
- A. Deploy your API as a Cloud Run service. Create a Pub/Sub topic, and configure your API to push messages to the topic.
- B. Deploy your API to App Engine. Create a Pub/Sub topic, and configure your API to push messages to the topic.
- C. Deploy your API on a Compute Engine instance. Create a Kafka cluster, and configure your API to write messages to the cluster.
- D. Deploy your API to a GKE cluster. Create a Kafka cluster, and configure your API to write messages to the cluster.
Correct Answer: A 🗳️
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You are planning to deploy hundreds of microservices in your Google Kubernetes Engine (GKE) cluster. How should you secure communication between the microservices on GKE using a managed service?
- A. Install cert-manager on GKE to automatically renew the SSL certificates.
- B. Use global HTTP(S) Load Balancing with managed SSL certificates to protect your services
- C. Deploy open source Istio in your GKE cluster, and enable mTLS in your Service Mesh
- D. Install Anthos Service Mesh, and enable mTLS in your Service Mesh.
Correct Answer: D 🗳️
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A governmental regulation was recently passed that affects your application. For compliance purposes, you are now required to send a duplicate of specific application logs from your application's project to a project that is restricted to the security team. What should you do?
- A. Modify the _Default log bucket sink rules to reroute the logs into the security team's log bucket.
- B. Create a job that copies the System Event logs from the _Required log bucket into the security team's log bucket in their project.
- C. Create a job that copies the logs from the _Required log bucket into the security team's log bucket in their project.
- D. Create user-defined log buckets in the security team's project. Configure a Cloud Logging sink to route your application's logs to log buckets in the security team's project.
Correct Answer: D 🗳️
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You have an application running in App Engine. Your application is instrumented with Stackdriver Trace. The /product-details request reports details about four known unique products at /sku- details as shown below. You want to reduce the time it takes for the request to complete.
What should you do?
- A. Store the /sku-details information in a database, and replace the webservice call with a database query.
- B. Change the Persistent Disk type to SSD.
- C. Change /product-details to perform the requests in parallel.
- D. Increase the size of the instance class.
Correct Answer: C 🗳️
You work for a financial services company that has a container-first approach. Your team develops microservices applications. You have a Cloud Build pipeline that creates a container image, runs regression tests, and publishes the image to Artifact Registry. You need to ensure that only containers that have passed the regression tests are deployed to GKE clusters. You have already enabled Binary Authorization on the GKE clusters. What should you do next?
- A. Create an attestor and a policy. Run a vulnerability scan to create an attestation for the container image as a step in the Cloud Build pipeline.
- B. Set the Pod Security Standard level to Restricted for the relevant namespaces. Digitally sign the container images that have passed the regression tests as a step in the Cloud Build pipeline.
- C. Deploy Voucher Server and Voucher Client components. After a container image has passed the regression tests, run Voucher Client as a step in the Cloud Build pipeline.
- D. Create an attestor and a policy. Create an attestation for the container images that have passed the regression tests as a step in the Cloud Build pipeline.
Correct Answer: D 🗳️
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