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Section 5: Managing Application Performance Monitoring
The last module of the certification test focuses on the ability of the applicants to perform the following technical tasks: managing Compute Engine Virtual Machines (VMs), managing Google Kubernetes Engine workloads, as well as troubleshooting the application performance.
Some of the questions included in the Google Professional Cloud Developer certification exam may guide you to a case study describing a fictitious solution and business concept. This will provide you with additional context to choose the right answer(s). You can get an idea of a possible case study by studying the example contained in the official study guide.
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Cloud technology is trending these days and it is easy to see why. Moreover, as the industry expands, the demand for cloud professionals raises too. Thus, the decision to become an accredited specialist in this field is worth all the efforts you are going to put into the process of getting certified. Now you know more about the Google Professional Cloud Developer exam, what you need to master to ace it successfully, and what are the best training courses and books that will help you prepare for the test. Futureproof your skills by obtaining your certificate today.
Reference: https://cloud.google.com/certification/cloud-developer
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The Google Professional Cloud Developer certificate is intended to validate the competence of IT professionals tasked with building highly available and scalable applications with the aid of Google endorsed practices and tools. Such individuals will have to demonstrate an understanding of developer tools, cloud-native applications, modern databases, and managed services during the corresponding exam. What’s more, targeted specialists need to have background experience working with at least one programming language and should be proficient in creating useful metrics and logs for debugging and trace coding.
Google Professional-Cloud-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing highly scalable, secure, and reliable cloud-native applications | 32% | - Designing for observability and monitoring
|
| Integrating applications with Google Cloud services | 21% | - Consuming Google Cloud APIs and services
|
| Configuring cloud-native applications for deployment | 24% | - Deploying applications to Cloud Run
|
| Building and testing applications | 23% | - Building applications and containers
|
Google Certified Professional - Cloud Developer Sample Questions:
You use Cloud Build to build and test container images prior to deploying them to Cloud Run.
Your images are stored in Artifact Registry. You need to ensure that only container images that have passed testing are deployed. You want to minimize operational overhead. What should you do?
- A. Enable Binary Authorization on your Cloud Run service. Create an attestation if the container image has passed all tests. Configure Binary Authorization to allow only images with appropriate attestation to be deployed to the Cloud Run service.
- B. Configure build provenance on your Cloud Build pipeline. Verify that all the tests have passed, and then deploy the image to a Cloud Run service.
- C. Deploy a new revision to a Cloud Run service. Assign a tag that allows access to the revision at a specific URL without serving traffic. Test that revision again. Migrate the traffic to the Cloud Run service after you confirm that the new revision is performing as expected.
- D. Create a GKE cluster. Verify that all tests have passed, and then deploy the image to the GKE cluster.
Correct Answer: A 🗳️
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Your company manages an application that captures stock data in an internal database. You need to create an API that provides real-time stock data to users. You want to return stock data to users as quickly as possible, and you want your solution to be highly scalable. What should you do?
- A. Create a Bigtable instance. Query the table when user requests are received. Configure a Pub/Sub topic to queue user requests that your API will respond to.
- B. Create a Memorystore for Redis instance, and use this database to store the most accessed stock data. Query this instance first when user requests are received, and fall back to the internal database.
- C. Create a BigQuery dataset and table to act as the internal database. Query the table when user requests are received.
- D. Create a Memorystore for Redis instance to store all stock market data. Query this database when user requests are received.
Correct Answer: B 🗳️
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You have developed a Python application that you want to containerize and deploy to Cloud Run.
You have developed a Cloud Build pipeline with the following steps:
After triggering the pipeline, you notice in the Cloud Build logs that the final step of the pipeline fails and the container is unable to be deployed to Cloud Run. What is the cause of this issue, and how should you resolve it?
- A. Cloud Run does not allow unauthenticated invocations. Remove the --allow-unauthenticated parameter to enforce authentication on the application, and rerun the pipeline.
- B. Unit tests in the pipeline are failing. Update the application code so that all unit tests pass, and rerun the pipeline.
- C. The Docker container image has not been pushed to Artifact Registry. Add a step to the pipeline to push the application container image to Artifact Registry, and rerun the pipeline.
- D. The final step uses a Cloud Run instance name that does not match the container name. Update the deployment step so that the Cloud Run instance name matches the container name, and rerun the pipeline.
Correct Answer: C 🗳️
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You are migrating a containerized application to Cloud Run. You plan to use Cloud Build to build your container image and push it to Artifact Registry, and you plan to use Cloud Deploy to deploy the image to production. You need to ensure that only secure images are deployed to production.
What should you do?
- A. Use Secret Manager to store the encrypted image. Deploy this image to production.
- B. Use Binary Authorization to enforce a policy that only allows images that have been signed with a trusted key to be deployed to production.
- C. Use Cloud Armor in front of Cloud Run to protect the container image from threats.
- D. Use Artifact Analysis to scan the image for vulnerabilities. Use Cloud Key Management Service to encrypt the image to be deployed to production.
Correct Answer: B 🗳️
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You have an application running on Google Kubernetes Engine (GKE). The application is currently using a logging library and is outputting to standard output. You need to export the logs to Cloud Logging, and you need the logs to include metadata about each request. You want to use the simplest method to accomplish this. What should you do?
- A. Update your application to output logs in CSV format, and add the necessary metadata to the CSV.
- B. Install the Fluent Bit agent on each of your GKE nodes, and have the agent export all logs from
/var/log. - C. Change your application's logging library to the Cloud Logging library, and configure your application to export logs to Cloud Logging.
- D. Update your application to output logs in JSON format, and add the necessary metadata to the JSON.
Correct Answer: D 🗳️
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