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| Section | Objectives |
|---|
| Ensuring application performance and reliability | - Performance optimization - Monitoring and logging (Cloud Monitoring, Cloud Logging) - Fault tolerance and incident response
|
| Integrating Google Cloud services | - API integration and service-to-service communication - Using managed services (Pub/Sub, Firestore, Cloud SQL)
|
| Designing cloud-native applications | - Scalability and resiliency patterns - Microservices design principles - Application architecture design
|
| Deploying applications | - Containerization and deployment (Cloud Run, GKE) - CI/CD pipelines - Release management strategies
|
| Building and testing applications | - Application development on Google Cloud - Debugging and troubleshooting applications - Unit and integration testing strategies
|
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Google Certified Professional - Cloud Developer Sample Questions (Q340-Q345):
NEW QUESTION # 340
Your teammate has asked you to review the code below. Its purpose is to query account entities in Cloud Datastore for those with a balance greater than 10000 and an age less than 4. Which improvement should you suggest your teammate make?
Captionless Image
- A. Send two queries-one for balances over 10000, and another for ages less than 4-and compute the intersection.
- B. The query should be performed in a transaction.
- C. Change the argument to OrderBy.desc to be "balance" instead of "age."
- D. The query needs to have an ancestor query.
Answer: A
Explanation:
A is not correct because the stated goal doesn't require an ancestor query.
B is not correct because no data is being written, so a transaction isn't necessary.
C is not correct because it wouldn't affect the results.
D is correct because two inequality comparisons aren't permitted in a Datastore query and it requires two queries to be merged.
NEW QUESTION # 341
You have an application deployed in Google Kubernetes Engine (GKE). You need to update the application to make authorized requests to Google Cloud managed services. You want this to be a one-time setup, and you need to follow security best practices of auto-rotating your security keys and storing them in an encrypted store. You already created a service account with appropriate access to the Google Cloud service. What should you do next?
- A. Assign the Google Cloud service account to your GKE Pod using Workload Identity.
- B. Export the Google Cloud service account, and share it with the Pod as a Kubernetes Secret.
- C. Export the Google Cloud service account, and embed it in the source code of the application.
- D. Export the Google Cloud service account, and upload it to HashiCorp Vault to generate a dynamic service account for your application.
Answer: A
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity Applications running on GKE might need access to Google Cloud APIs such as Compute Engine API, BigQuery Storage API, or Machine Learning APIs.
Workload Identity allows a Kubernetes service account in your GKE cluster to act as an IAM service account.
Pods that use the configured Kubernetes service account automatically authenticate as the IAM service account when accessing Google Cloud APIs. Using Workload Identity allows you to assign distinct, fine-grained identities and authorization for each application in your cluster.
NEW QUESTION # 342
You work for a web development team at a small startup. Your team is developing a Node.js application using Google Cloud services, including Cloud Storage and Cloud Build. The team uses a Git repository for version control. Your manager calls you over the weekend and instructs you to make an emergency update to one of the company's websites, and you're the only developer available. You need to access Google Cloud to make the update, but you don't have your work laptop. You are not allowed to store source code locally on a non-corporate computer.
How should you set up your developer environment?
- A. Use a text editor and the Git command line to send your source code updates as pull requests from a public computer.
- B. Use a Cloud Storage bucket to store the source code that you need to edit. Mount the bucket to a public computer as a drive, and use a code editor to update the code. Turn on versioning for the bucket, and point it to the team's Git repository.
- C. Use a text editor and the Git command line to send your source code updates as pull requests from a virtual machine running on a public computer.
- D. Use Cloud Shell and the built-in code editor for development. Send your source code updates as pull requests.
Answer: D
Explanation:
https://cloud.google.com/shell/docs
loud Shell is an interactive shell environment for Google Cloud that lets you learn and experiment with Google Cloud and manage your projects and resources from your web browser.
With Cloud Shell, the Google Cloud CLI and other utilities you need are pre-installed, fully authenticated, up-to-date, and always available when you need them. Cloud Shell comes with a built-in code editor with an integrated Cloud Code experience, allowing you to develop, build, debug, and deploy your cloud-based apps entirely in the cloud.
NEW QUESTION # 343
You developed a JavaScript web application that needs to access Google Drive's API and obtain permission from users to store files in their Google Drives. You need to select an authorization approach for your application. What should you do?
- A. Create a SAML token.
- B. Create a service account.
- C. Create an API key.
- D. Create an OAuth Client ID.
Answer: D
Explanation:
https://developers.google.com/drive/api/guides/api-specific-auth
NEW QUESTION # 344
Your team is using App Engine to write every Cloud Pub/Sub message to both a Cloud Storage object and a BigQuery table. You want to achieve the greatest resource efficiency. Which architecture should you implement?

Answer: B
Explanation:
A is not correct because this will write half the messages to BigQuery and half to Cloud Storage, which doesn't meet the need.
B is correct because each App Engine service will get its own message to write and can retry/fail independently.
C is not correct because a failure in processing the message to one system can cause duplicate writes to BigQuery or Google Cloud Storage.
D is not correct because it duplicates the message send charges and requires that the message be sent twice to two different topics, which increases costs and complexities on the client.
NEW QUESTION # 345
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