Spring Sale Special Limited Time 70% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: xmas50

Google Professional-Cloud-Architect - Google Certified Professional - Cloud Architect (GCP)

Google Professional-Cloud-Architect Premium Access     Download Demo    
Page: 6 / 6
Total 333 questions

For this question, refer to the Mountkirk Games case study.

Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements:

• Services are deployed redundantly across multiple regions in the US and Europe.

• Only frontend services are exposed on the public internet.

• They can provide a single frontend IP for their fleet of services.

• Deployment artifacts are immutable.

Which set of products should they use?

A.

Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

B.

Google Cloud Storage, Google App Engine, Google Network Load Balancer

C.

Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

D.

Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

For this question, refer to the Mountkirk Games case study

Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments. Developers and testers can access each other's environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production.

What should you do to isolate development environments from staging and production?

A.

Create a project for development and test and another for staging and production.

B.

Create a network for development and test and another for staging and production.

C.

Create one subnetwork for development and another for staging and production.

D.

Create one project for development, a second for staging and a third for production.

For this question, refer to the Mountkirk Games case study.

Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a thorough testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?

A.

Create a scalable environment in GCP for simulating production load.

B.

Use the existing infrastructure to test the GCP-based backend at scale.

C.

Build stress tests into each component of your application using resources internal to GCP to simulate load.

D.

Create a set of static environments in GCP to test different levels of load — for example, high, medium, and low.

For this question, refer to the Mountkirk Games case study.

Mountkirk Games' gaming servers are not automatically scaling properly. Last month, they rolled out a new feature, which suddenly became very popular. A record number of users are trying to use the service, but many of them are getting 503 errors and very slow response times. What should they investigate first?

A.

Verify that the database is online.

B.

Verify that the project quota hasn't been exceeded.

C.

Verify that the new feature code did not introduce any performance bugs.

D.

Verify that the load-testing team is not running their tool against production.

For this question, refer to the Mountkirk Games case study.

Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

A.

Tests should scale well beyond the prior approaches.

B.

Unit tests are no longer required, only end-to-end tests.

C.

Tests should be applied after the release is in the production environment.

D.

Tests should include directly testing the Google Cloud Platform (GCP) infrastructure.

TerramEarth has about 1 petabyte (PB) of vehicle testing data in a private data center. You want to move the data to Cloud Storage for your machine learning team. Currently, a 1-Gbps interconnect link is available for you. The machine learning team wants to start using the data in a month. What should you do?

A.

Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.

B.

Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage

C.

Make sure there are no other users consuming the 1 Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.

D.

Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage