Google Associate Cloud Engineer Practice Test - Questions Answers, Page 29
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Your company requires that Google Cloud products are created with a specific configuration to comply with your company's security policies You need to implement a mechanism that will allow software engineers at your company to deploy and update Google Cloud products in a preconfigured and approved manner. What should you do?
Create Java packages that utilize the Google Cloud Client Libraries for Java to configure Google Cloud products. Store and share the packages in a source code repository.
Create bash scripts that utilize the Google Cloud CLI to configure Google Cloud products. Store and share the bash scripts in a source code repository.
Create Terraform modules that utilize the Google Cloud Terraform Provider to configure Google Cloud products. Store and share the modules in a source code repository.
Use the Google Cloud APIs by using curl to configure Google Cloud products. Store and share the curl commands in a source code repository.
You want to enable your development team to deploy new features to an existing Cloud Run service in production. To minimize the risk associated with a new revision, you want to reduce the number of customers who might be affected by an outage without introducing any development or operational costs to your customers. You want to follow Google-recommended practices for managing revisions to a service. What should you do?
Deploy your application to a second Cloud Run service, and ask your customers to use the second Cloud Run service.
Ask your customers to retry access to your service with exponential backoff to mitigate any potential problems after the new revision is deployed.
Gradually roll out the new revision and split customer traffic between the revisions to allow rollback in case a problem occurs.
Send all customer traffic to the new revision, and roll back to a previous revision if you witness any problems in production.
You have two Google Cloud projects: project-a with VPC vpc-a (10.0.0.0/16) and project-b with VPC vpc-b (10.8.0.0/16). Your frontend application resides in vpc-a and the backend API services ate deployed in vpc-b. You need to efficiently and cost-effectively enable communication between these Google Cloud projects. You also want to follow Google-recommended practices. What should you do?
Configure a Cloud Router in vpc-a and another Cloud Router in vpc-b.
Configure a Cloud Interconnect connection between vpc-a and vpc-b.
Create VPC Network Peering between vpc-a and vpc-b.
Create an OpenVPN connection between vpc-a and vpc-b.
You have a VM instance running in a VPC with single-stack subnets. You need to ensure that the VM instance has a fixed IP address so that other services hosted in the same VPC can communicate with the VM. You want to follow Google-recommended practices while minimizing cost. What should you do?
Reserve a new static external IP address and assign the new IP address to the VM.
Promote the existing IP address of the VM to become a static external IP address.
Reserve a new static external IPv6 address and assign the new IP address to the VM.
Promote the existing IP address of the VM to become a static internal IP address.
Your company uses BigQuery to store and analyze data. Upon submitting your query in BigQuery, the query fails with a quotaExceeded error. You need to diagnose the issue causing the error. What should you do?
Choose 2 answers
Search errors in Cloud Audit Logs to analyze the issue.
Configure Cloud Trace to analyze the issue.
View errors in Cloud Monitoring to analyze the issue.
Use the information schema views to analyze the underlying issue.
Use BigQuery Bl Engine to analyzetheissue.
Your web application is hosted on Cloud Run and needs to query a Cloud SOL database. Every morning during a traffic spike, you notice API quota errors in Cloud SOL logs. The project has already reached the maximum API quota. You want to make a configuration change to mitigate the issue. What should you do?
Modify the minimum number of Cloud Run instances.
Set a minimum concurrent requests environment variable for the application.
Modify the maximum number of Cloud Run instances.
Use traffic splitting.
You are planning to migrate the following on-premises data management solutions to Google Cloud:
* One MySQL cluster for your main database
* Apache Kafka for your event streaming platform
* One Cloud SOL for PostgreSOL database for your analytical and reporting needs
You want to implement Google-recommended solutions for the migration. You need to ensure that the new solutions provide global scalability and require minimal operational and infrastructure management. What should you do?
Migrate from MySQL to Cloud SQL, from Kafka to Memorystore, and from Cloud SQL for PostgreSQL to Cloud SQL
Migrate from MySQL to Cloud Spanner, from Kafka to Memorystore, and from Cloud SOL for PostgreSQL to Cloud SQL
Migrate from MySQL to Cloud SOL, from Kafka to Pub/Sub, and from Cloud SOL for PostgreSQL to BigQuery.
Migrate from MySQL to Cloud Spanner, from Kafka to Pub/Sub. and from Cloud SQL for PostgreSQL to BigQuery
You need to deploy a single stateless web application with a web interface and multiple endpoints. For security reasons, the web application must be reachable from an internal IP address from your company's private VPC and on-premises network. You also need to update the web application multiple times per day with minimal effort and want to manage a minimal amount of cloud infrastructure. What should you do?
Deploy the web application on Google Kubernetes Engine standard edition with an internal ingress.
Deploy the web application on Cloud Run with Private Google Access configured
Deploy the web application to GKE Autopilot with Private Google Access configured
Deploy the web application on Cloud Run with Private Service Connect configured.
You use Cloud Logging lo capture application logs. You now need to use SOL to analyze the application logs in Cloud Logging, and you want to follow Google-recommended practices. What should you do?
Develop SQL queries by using Gemini for Google Cloud.
Enable Log Analytics for the log bucket and create a linked dataset in BigQuery.
Create a schema for the storage bucket and run SQL queries for the data in the bucket.
Export logs to a storage bucket and create an external view in BigQuery.
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