Question 1

You work at a rapidly growing financial technology startup. You manage the payment processing applicationwritten in Go and hosted on Cloud Run in the Singapore region (asia-southeast1). The payment processingapplication processes data stored in a Cloud Storage bucket that is also located in the Singapore region.The startup plans to expand further into the Asia Pacific region. You plan to deploy the Payment Gateway inJakarta, Hong Kong, and Taiwan over the next six months. Each location has data residency requirements thatrequire customer data to reside in the country where the transaction was made. You want to minimize the costof these deployments. What should you do?

Options :

A : Create a Cloud Storage bucket in each region, and create a Cloud Run service of the payment processing application in each region

B : Create a Cloud Storage bucket in each region, and create three Cloud Run services of the payment processing application in the Singapore region.

C : Create three Cloud Storage buckets in the Asia multi-region, and create three Cloud Run services of the payment processing application in the Singapore region

D : Create three Cloud Storage buckets in the Asia multi-region, and create three Cloud Run revisions of the payment processing application in the Singapore region.

Answer: A

Question 2

You are developing a new application that has the following design requirements:Creation and changes to the application infrastructure are versioned and auditable.The application and deployment infrastructure uses Google-managed services as much as possible.The application runs on a serverless compute platform.How should you design the application’s architecture?

Options :

A : 1. Store the application and infrastructure source code in a Git repository.2. Use Cloud Build to deploy the application infrastructure with Terraform.3. Deploy the application to a Cloud Function as a pipeline step.

B : 1. Deploy Jenkins from the Google Cloud Marketplace, and define a continuous integration pipeline inJenkins.2. Configure a pipeline step to pull the application source code from a Git repository.3. Deploy the application source code to App Engine as a pipeline step.

C : 1. Create a continuous integration pipeline on Cloud Build, and configure the pipeline to deploy theapplication infrastructure using Deployment Manager templates.2. Configure a pipeline step to create a container with the latest application source code.3. Deploy the container to a Compute Engine instance as a pipeline step.

D : 1. Deploy the application infrastructure using gcloud commands.2. Use Cloud Build to define a continuous integration pipeline for changes to the application source code.3. Configure a pipeline step to pull the application source code from a Git repository, and create acontainerized application.4. Deploy the new container on Cloud Run as a pipeline step.

Answer: D

Question 3

Your company's development teams want to use various open source operating systems in their Docker builds. When images are created in published containers in your company's environment, you need to scan them for Common Vulnerabilities and Exposures (CVEs) The scanning process must not impact software development agility. You want to use managed services where possible. What should you do?

Options :

A : Enable the Vulnerability scanning setting in the Container Registry.

B : Create a Cloud Function that is triggered on a code check-in and scan the code for CVEs.

C : Disallow the use of non-commercially supported base images in your development environment

D : Use Cloud Monitoring to review the output of Cloud Build to determine whether a vulnerable version has been used

Answer: A

Question 4

To perform unit and integration tests during the Docker image build process using Cloud Build, you need to make modifications. In the event of a failure, you want the build history to clearly indicate the stage at which the build failed.What actions do you need to take?

Options :

A : Create a Cloud Build build config file with a single build step to compile unit and integration tests.

B : Create a Cloud Build build config file that will spawn a separate cloud build pipeline for unit and integration tests.

C : Add RUN commands in the Dockerfile to execute unit and integration tests.

D : Create a Cloud Build build config file with separate cloud builder steps to compile and execute unit and integration tests.

Answer: D

Question 5

The analytics system is currently facing a permission error while executing queries against a BigQuery dataset. The SQL query is executed in batch by passing the contents of a SQL file to the BigQuery CLI, which in turn redirects the output to another process. To resolve this issue, you need to identify the root cause of the permission error and take necessary steps to fix it.What actions are recommended in this situation?

Options :

A : Create a view in BigQuery from an SQL query and SELECT* from the view in the command-line interface.

B : Grant the service account BigQuery Data Editor and BigQuery Data Viewer roles.

C : Grant the service account BigQuery Data Viewer and BigQuery Job User roles.

D : Create a new dataset in BigQuery, copy the source table to the new dataset, and query the new dataset and table from the command-line interface.

Answer: C

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