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Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Developer Sample Questions (Q332-Q337):

NEW QUESTION # 332
Your development team has built several Cloud Functions using Java along with corresponding integration and service tests. You are building and deploying the functions and launching the tests using Cloud Build.
Your Cloud Build job is reporting deployment failures immediately after successfully validating the code.
What should you do?

A. Verify that the Cloud Build service account is assigned the Cloud Functions Developer role.
B. Retry the tests using the truncated exponential backoff polling strategy.
C. Check the maximum number of Cloud Function instances.
D. Verify that your Cloud Build trigger has the correct build parameters.

Answer: A

Explanation:
Explanation
https://cloud.google.com/build/docs/securing-builds/configure-access-for-cloud-build-service-account

NEW QUESTION # 333
You migrated your applications to Google Cloud Platform and kept your existing monitoring platform. You now find that your notification system is too slow for time critical problems.
What should you do?

A. Use Stackdriver to capture and alert on logs, then ship them to your existing platform.
B. Install the Stackdriver agents on your Compute Engine instances.
C. Replace your entire monitoring platform with Stackdriver.
D. Migrate some traffic back to your old platform and perform AB testing on the two platforms concurrently.

Answer: B

Explanation:
Explanation/Reference: https://cloud.google.com/monitoring/

NEW QUESTION # 334
From the following, select the best description of cosmetic condition that would be included in good case notes.

A. Big ding; probably dropped or thrown
B. Case is dented like it was dropped
C. Badly dinged in addition to crack
D. Significant dent in lower right corner suggests impact

Answer: D

NEW QUESTION # 335
You manage an application that runs in a Compute Engine instance. You also have multiple backend services executing in stand-alone Docker containers running in Compute Engine instances. The Compute Engine instances supporting the backend services are scaled by managed instance groups in multiple regions. You want your calling application to be loosely coupled. You need to be able to invoke distinct service implementations that are chosen based on the value of an HTTP header found in the request. Which Google Cloud feature should you use to invoke the backend services?

A. Service Directory
B. Traffic Director
C. Anthos Service Mesh
D. Internal HTTP(S) Load Balancing

Answer: B

Explanation:
https://cloud.google.com/traffic-director/docs/overview#traffic_management Advanced traffic management, including routing and request manipulation (based on hostname, path, headers, cookies, and more), enables you to determine how traffic flows between your services. You can also apply actions like retries, redirects, and weight-based traffic splitting for canary deployments. Advanced patterns like fault injection, traffic mirroring, and outlier detection enable DevOps use cases that improve your resiliency.

NEW QUESTION # 336
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 Horizontal Pod Autoscaler, and scale based on the CPU toad.
B. Deploy a Horizontal Pod Autoscaler, and scale based on a custom metric.
C. Deploy a Vertical Pod Autoscaler, and scale based on a custom metric.
D. Deploy a Vertical Pod Autoscaler, and scale based on the CPU load.

Answer: A

Explanation:
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 # 337
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