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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Topic 1: Deploying applications	- Containerization and deployment (Cloud Run, GKE) - Release management strategies - CI/CD pipelines
Topic 2: Ensuring application performance and reliability	- Fault tolerance and incident response - Monitoring and logging (Cloud Monitoring, Cloud Logging) - Performance optimization
Topic 3: Building and testing applications	- Application development on Google Cloud - Unit and integration testing strategies - Debugging and troubleshooting applications
Topic 4: Designing cloud-native applications	- Microservices design principles - Scalability and resiliency patterns - Application architecture design
Topic 5: Integrating Google Cloud services	- Using managed services (Pub/Sub, Firestore, Cloud SQL) - API integration and service-to-service communication

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Google Certified Professional - Cloud Developer Sample Questions (Q328-Q333):

NEW QUESTION # 328
Your application is running on Compute Engine and is showing sustained failures for a small number of requests. You have narrowed the cause down to a single Compute Engine instance, but the instance is unresponsive to SSH.
What should you do next?

A. Enable and check the serial port output.
B. Take a snapshot of the disk and attach it to a new machine.
C. Delete the machine and create a new one.
D. Reboot the machine.

Answer: A

Explanation:
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-
ssh#debug_with_serial_console

NEW QUESTION # 329
You deployed a new application to Google Kubernetes Engine and are experiencing some performance degradation. Your logs are being written to Cloud Logging, and you are using a Prometheus sidecar model for capturing metrics. You need to correlate the metrics and data from the logs to troubleshoot the performance issue and send real-time alerts while minimizing costs. What should you do?

A. Create custom metrics from the Cloud Logging logs, and use Prometheus to import the results using the Cloud Monitoring REST API.
B. Export the Prometheus metrics and use Cloud Monitoring to view them as external metrics. Configure Cloud Monitoring to create log-based metrics from the logs, and correlate them with the Prometheus data.
C. Export the Cloud Logging logs and the Prometheus metrics to Cloud Bigtable. Run a query to join the results, and analyze in Google Data Studio.
D. Export the Cloud Logging logs and stream the Prometheus metrics to BigQuery. Run a recurring query to join the results, and send notifications using Cloud Tasks.

Answer: B

Explanation:
Reference:
https://cloud.google.com/blog/products/operations/troubleshoot-gke-faster-with-monitoring-data-in-your-logs

NEW QUESTION # 330
You want to re-architect a monolithic application so that it follows a microservices model. You want to accomplish this efficiently while minimizing the impact of this change to the business.
Which approach should you take?

A. Build a new application with the appropriate microservices separate from the monolith and replace it when it is complete.
B. Refactor the monolithic application with appropriate microservices in a single effort and deploy it.
C. Replace the application's features with appropriate microservices in phases.
D. Deploy the application to Compute Engine and turn on autoscaling.

Answer: B

Explanation:
Reference:
https://cloud.google.com/solutions/migrating-a-monolithic-app-to-microservices-gke

NEW QUESTION # 331
You are developing a JPEG image-resizing API hosted on Google Kubernetes Engine (GKE). Callers of the service will exist within the same GKE cluster. You want clients to be able to get the IP address of the service.
What should you do?

A. Define a GKE Endpoint. Clients should get the endpoint name from the appropriate environment variable in the client container.
B. Define a GKE Service. Clients should use the name of the A record in Cloud DNS to find the service's cluster IP address.
C. Define a GKE Endpoint. Clients should get the endpoint name from Cloud DNS.
D. Define a GKE Service. Clients should use the service name in the URL to connect to the service.

Answer: A

NEW QUESTION # 332
You work for an organization that manages an ecommerce site. Your application is deployed behind a global HTTP(S) load balancer. You need to test a new product recommendation algorithm. You plan to use A/B testing to determine the new algorithm's effect on sales in a randomized way. How should you test this feature?

A. Split traffic between versions using weights.
B. Enable the new recommendation feature flag on a single instance.
C. Mirror traffic to the new version of your application.
D. Use HTTP header-based routing.

Answer: C

Explanation:
https://cloud.google.com/load-balancing/docs/https/traffic-management-global#traffic_actions_weight-based_tra Deploying a new version of an existing production service generally incurs some risk. Even if your tests pass in staging, you probably don't want to subject 100% of your users to the new version immediately. With traffic management, you can define percentage-based traffic splits across multiple backend services.
For example, you can send 95% of the traffic to the previous version of your service and 5% to the new version of your service. After you've validated that the new production version works as expected, you can gradually shift the percentages until 100% of the traffic reaches the new version of your service. Traffic splitting is typically used for deploying new versions, A/B testing, service migration, and similar processes.
https://cloud.google.com/traffic-director/docs/advanced-traffic-management#weight-based_traffic_splitting_for_
https://cloud.google.com/architecture/implementing-deployment-and-testing-strategies-on-gke#split_the_traffic_
https://cloud.google.com/load-balancing/docs/https/traffic-management-global#traffic_actions_weight-based_tra

NEW QUESTION # 333
......

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