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Google Professional-Cloud-Developer, also known as Google Certified Professional - Cloud Developer, is a certification exam offered by Google. Professional-Cloud-Developer Exam is designed to test the candidate's knowledge and skills in developing, designing, and managing applications using Google Cloud Platform. Professional-Cloud-Developer exam is intended for developers who have experience in developing and deploying applications on Google Cloud Platform.

Google Professional Cloud Developer Certified Professional salary

The average salary of a Google Professional Cloud Developer Certified Expert in

India - 27,42,327 INR
England - 135,632 POUND
Europe - 145,347 EURO
United State - 181,247 USD

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Incorporating Google Cloud Services

Integrate Cloud APIs with Apps: This domain comes with caching results, restricting return data, batching requests, paginating results, and error handling.
Incorporate Applications with Data & Storage Services: The considerations for this subsection include connecting to data stores, write/read data from/to different databases and write applications that consume/publish data asynchronously.
Integrate Applications with Compute Services: This part contains the measurement of the skills in implementing the service discovery in Compute Engine and GKE, authenticating utilizing Identity-Aware Proxy & OAuth2.0 Web Flow, and authenticating with Workload Identity to Cloud APIs.

Google Certified Professional - Cloud Developer Sample Questions (Q259-Q264):

NEW QUESTION # 259
You are deploying your application to a Compute Engine virtual machine instance with the Stackdriver Monitoring Agent installed. Your application is a unix process on the instance. You want to be alerted if the unix process has not run for at least 5 minutes. You are not able to change the application to generate metrics or logs.
Which alert condition should you configure?

A. Metric threshold
B. Metric absence
C. Process health
D. Uptime check

Answer: C

Explanation:
Reference: https://cloud.google.com/monitoring/alerts/concepts-indepth

NEW QUESTION # 260
You are building an API that will be used by Android and iOS apps. The API must:
- Support HTTPs
- Minimize bandwidth cost
- Integrate easily with mobile apps
Which API architecture should you use?

A. SOAP-based APIs
B. RESTful APIs
C. gRPC-based APIs
D. MQTT for APIs

Answer: C

Explanation:
gRPC architectural style has promising features that can (and should) be explored. It is an excellent option for working with multi-language systems, real-time streaming, and for instance, when operating an IoT system that requires light-weight message transmission such as the serialized Protobuf messages allow. Moreover, gRPC should also be considered for mobile applications since they do not need a browser and can benefit from smaller messages, preserving mobiles' processors' speed.

NEW QUESTION # 261
You are developing an application that consists of several microservices running in a Google Kubernetes Engine cluster. One microservice needs to connect to a third-party database running on-premises. You need to store credentials to the database and ensure that these credentials can be rotated while following security best practices. What should you do?

A. Store the credentials in an encrypted volume mount, and associate a Persistent Volume Claim with the client Pod.
B. Store the credentials in a sidecar container proxy, and use it to connect to the third-party database.
C. Configure a service mesh to allow or restrict traffic from the Pods in your microservice to the database.
D. Store the credentials as a Kubernetes Secret, and use the Cloud Key Management Service plugin to handle encryption and decryption.

Answer: D

Explanation:
Explanation
https://cloud.google.com/kubernetes-engine/docs/how-to/encrypting-secrets By default, Google Kubernetes Engine (GKE) encrypts customer content stored at rest, including Secrets.
GKE handles and manages this default encryption for you without any additional action on your part.
Application-layer secrets encryption provides an additional layer of security for sensitive data, such as Secrets, stored in etcd. Using this functionality, you can use a key managed with Cloud KMS to encrypt data at the application layer. This encryption protects against attackers who gain access to an offline copy of etcd.

NEW QUESTION # 262
Your application is composed of a set of loosely coupled services orchestrated by code executed on Compute Engine. You want your application to easily bring up new Compute Engine instances that find and use a specific version of a service. How should this be configured?

A. Define your service endpoint information to be retrieved from an environment variable at runtime and used to connect to the desired service.
B. Define your service endpoint information as metadata that is retrieved at runtime and used to connect to the desired service.
C. Define your service to use a fixed hostname and port to connect to the desired service. Replace the service at the endpoint with your new version.
D. Define your service endpoint information as label data that is retrieved at runtime and used to connect to the desired service.

Answer: B

Explanation:
Explanation
https://cloud.google.com/service-infrastructure/docs/service-metadata/reference/rest#service-endpoint

NEW QUESTION # 263
You are developing an online gaming platform as a microservices application on Google Kubernetes Engine (GKE). Users on social media are complaining about long loading times for certain URL requests to the application. You need to investigate performance bottlenecks in the application and identify which HTTP requests have a significantly high latency span in user requests. What should you do?

A. Instrument your microservices by installing the OpenTelemetry tracing package. Update your application code to send traces to Trace for inspection and analysis. Create an analysis report on Trace to analyze user requests.
B. Install tcpdump on your GKE nodes. Run tcpdump to capture network traffic over an extended period of time to collect data. Analyze the data files using Wireshark to determine the cause of high latency.
C. Update your microservices to log HTTP request methods and URL paths to STDOUT. Use the logs router to send container logs to Cloud Logging. Create filters in Cloud Logging to evaluate the latency of user requests across different methods and URL paths.
D. Configure GKE workload metrics using kubectl. Select all Pods to send their metrics to Cloud Monitoring. Create a custom dashboard of application metrics in Cloud Monitoring to determine performance bottlenecks of your GKE cluster.

Answer: A

Explanation:
https://cloud.google.com/trace/docs/setup#when-to-instrument

NEW QUESTION # 264
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