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The Google Certified Professional - Cloud Developer is ideal whether you're just beginning your career in open source or planning to advance your career. Moreover, the Google Certified Professional - Cloud Developer also serves as a great stepping stone to earning advanced Google Certified Professional - Cloud Developer. Success in the Professional-Cloud-Developer exam is the basic requirement to get the a good job. You get multiple career benefits after cracking the Google Certified Professional - Cloud Developer. These benefits include skills approval, high-paying jobs, and promotions. Read on to find more important details about the Google Professional-Cloud-Developer Exam Questions.

Google Professional-Cloud-Developer Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Developer Sample Questions (Q152-Q157):

NEW QUESTION # 152
You are deploying a containerized application to GKE. You have set up a build pipeline by using Cloud Build that builds a Java application and pushes the application container image to Artifact Registry. Your build pipeline executes multiple sequential steps that reference Docker container images with the same layers. You notice that the Cloud Build pipeline runs are taking longer than expected to complete. How should you optimize the Docker image build process?

Answer: D

Explanation:
Using the --cache-from argument enables Docker layer caching by specifying a previous image as a cache source. This allows Cloud Build to reuse layers from a previously built image, significantly reducing build time, especially when layers have not changed between builds. This approach is effective in optimizing Docker image builds and is a recommended best practice for reducing build times in Cloud Build.
- The --squash parameter is no longer supported in Docker and does not directly improve build speed.
- Using private pools in Cloud Build or Cloud CDN for container artifacts does not directly address build efficiency and caching in Docker builds.


NEW QUESTION # 153
You have a container deployed on Google Kubernetes Engine. The container can sometimes be slow to launch, so you have implemented a liveness probe. You notice that the liveness probe occasionally fails on launch. What should you do?

Answer: D

Explanation:
https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#configure-


NEW QUESTION # 154
You need to configure a Deployment on Google Kubernetes Engine (GKE). You want to include a check that verifies that the containers can connect to the database. If the Pod is failing to connect, you want a script on the container to run to complete a graceful shutdown. How should you configure the Deployment?

Answer: C

Explanation:
Explanation
https://cloud.google.com/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke#m


NEW QUESTION # 155
You have a mixture of packaged and internally developed applications hosted on a Compute Engine instance that is running Linux. These applications write log records as text in local files.
You want the logs to be written to Cloud Logging. What should you do?

Answer: D

Explanation:
https://cloud.google.com/stackdriver/docs/solutions/agents/ops-agent
The Ops Agent is the primary agent for collecting telemetry from your Compute Engine instances.
Combining logging and metrics into a single agent, the Ops Agent uses Fluent Bit for logs, which supports high-throughput logging, and the OpenTelemetry Collector for metrics.


NEW QUESTION # 156
Your teammate has asked you to review the code below. Its purpose is to efficiently add a large number of small rows to a BigQuery table.

Which improvement should you suggest your teammate make?

Answer: A


NEW QUESTION # 157
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