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Google Professional-Cloud-Developer Exam Syllabus Topics:

Section	Objectives
Deploy applications	- Deploy applications to Google Cloud 1. Manage application rollout strategies 2. Deploy applications using Cloud Run 3. Deploy applications to Google Kubernetes Engine
Manage application performance monitoring	- Monitor and troubleshoot applications 1. Optimize application performance 2. Configure logging and tracing 3. Analyze metrics and alerts
Design scalable, available, and reliable cloud-native applications	- Design highly performant applications and APIs 1. Design resilient and scalable application architectures 2. Choose storage options for application data 3. Design secure applications
Integrate applications with Google Cloud services	- Integrate with data and storage services 1. Integrate with Google Cloud APIs 2. Use messaging and event-driven services 3. Implement authentication and authorization
Build and test applications	- Manage development environments 1. Perform application testing 2. Implement CI/CD pipelines 3. Build application artifacts

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Google Certified Professional - Cloud Developer Sample Questions (Q349-Q354):

NEW QUESTION # 349
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. Export the Google Cloud service account, and share it with the Pod as a Kubernetes Secret.
B. Export the Google Cloud service account, and upload it to HashiCorp Vault to generate a dynamic service account for your application.
C. Export the Google Cloud service account, and embed it in the source code of the application.
D. Assign the Google Cloud service account to your GKE Pod using Workload Identity.

Answer: D

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 # 350
You are designing a microservices architecture for a new application that will be deployed on Cloud Run. The application requires high-throughput communication between the internal microservices. You want to use the most effective, lowest latency communication protocol for this application. What should you do?

A. Configure the Cloud Run service to use HTTP/2. Implement gRPC for communication between the microservices. Use streaming gRPCs when a large amount of data has to be sent.
B. Use SOAP to build the microservices API, and use XML as the data format for communication across the microservices. Define SOAP data contracts for each microservice.
C. Use HTTP REST to communicate across the microservices. Implement pagination and add indexing to your database.
D. Implement the microservices with the REST API communication protocol. Use Apigee with rate- limiting to provide the best QoS for high-priority services.

Answer: A

Explanation:
gRPC over HTTP/2 is a high-performance communication protocol that is well-suited for high- throughput, low-latency communication between microservices. It supports features like bidirectional streaming, which is beneficial when large amounts of data need to be transferred.
gRPC is designed for inter-service communication in microservices architectures and allows for faster serialization and deserialization using Protocol Buffers, making it more efficient than traditional HTTP REST or SOAP for high-throughput applications.
Using gRPC with HTTP/2 in Cloud Run provides better performance and lower latency compared to REST over HTTP/1.1, which is less efficient for high-throughput, low-latency requirements.

NEW QUESTION # 351
Your company manages an application that captures stock data in an internal database. You need to create an API that provides real-time stock data to users. You want to return stock data to users as quickly as possible, and you want your solution to be highly scalable. What should you do?

A. Create a Memorystore for Redis instance to store all stock market data. Query this database when user requests are received.
B. Create a Bigtable instance. Query the table when user requests are received. Configure a Pub/Sub topic to queue user requests that your API will respond to.
C. Create a Memorystore for Redis instance, and use this database to store the most accessed stock data. Query this instance first when user requests are received, and fall back to the internal database.
D. Create a BigQuery dataset and table to act as the internal database. Query the table when user requests are received.

Answer: C

Explanation:
Using Memorystore for Redis as a caching layer for frequently accessed stock data allows you to deliver data with minimal latency, which is crucial for real-time applications. Redis is an in- memory data store that provides very fast read and write operations, making it well-suited for scenarios where low latency is essential.
By querying Redis first, you reduce the load on the internal database and speed up response times for popular data. For requests not in the cache, you can fall back to the internal database, ensuring that all data remains accessible without needing to query a potentially slower database for every request. This approach is highly scalable and balances speed with data availability.

NEW QUESTION # 352
You are developing a new web application using Cloud Run and committing code to Cloud Source Repositories. You want to deploy new code in the most efficient way possible. You have already created a Cloud Build YAML file that builds a container and runs the following command: gcloud run deploy. What should you do next?

A. Create a Cron job that runs the following command every 24 hours: gcloud builds submit.
B. Create a build trigger that runs the build file in response to a repository code being pushed to the development branch.
C. Create a webhook build trigger that runs the build file in response to HTTP POST calls to the webhook URL.
D. Create a Pub/Sub topic to be notified when code is pushed to the repository. Create a Pub/Sub trigger that runs the build file when an event is published to the topic.

Answer: B

Explanation:
Explanation
https://cloud.google.com/build/docs/triggers
Cloud Build uses build triggers to enable CI/CD automation. You can configure triggers to listen for incoming events, such as when a new commit is pushed to a repository or when a pull request is initiated, and then automatically execute a build when new events come in. You can also configure triggers to build code on any changes to your source repository or only on changes that match certain criteria.

NEW QUESTION # 353
You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale. What should you do?

A. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
B. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
C. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.
D. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Answer: D

Explanation:
Explanation
https://cloud.google.com/architecture/distributed-load-testing-using-gke

NEW QUESTION # 354
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