Google Professional-Cloud-Architect 問題集

質問 1：
You need to build a continuous delivery pipeline for a containerized application in Google Cloud.
You want to run all your tests in the pipeline to improve your application's quality. What should you do?
A. 1. After the developers push the code to a central repository, trigger Cloud Build to run unit tests.
If all unit tests are successful, build the application container and push it to a central registry.
2. Trigger Cloud Build to deploy the container to a testing environment and run integration tests and acceptance tests.
3. If all tests are successful, deploy the application to the production environment and run the smoke tests.
B. 1. After the developers push the code to a central repository, trigger Cloud Build to build the application container. Then run unit tests.
2. If unit tests are successful, deploy the container to a testing environment and run integration tests.
3. If the integration tests are successful, deploy the container to a production environment and run acceptance tests.
C. 1. Automatically run unit tests in a local environment once the code is changed. If all tests are successful, build a container.
2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests.
3. If all tests are successful, tag the code as production ready. Trigger Cloud Build to build and deploy the container to the production environment.
D. 1. Run unit tests in the developer's local environment before committing and pushing the code to a central repository.
2. After the code is pushed, trigger Cloud Build to build the application container and deploy the container to a testing environment, and run integration tests.
3. If the integration tests are successful, deploy the container to your production environment and run acceptance tests.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
Case Study: 8 - Mountkirk Games, C
Company overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud.
Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena.
Solution concept
Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game's backend on Google Kubernetes Engine so they can scale rapidly and use Google's global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster.
Existing technical environment
The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project.
There are also separate environments for development and testing.
Business requirements
- Support multiple gaming platforms.
- Support multiple regions.
- Support rapid iteration of game features.
- Minimize latency.
- Optimize for dynamic scaling.
- Use managed services and pooled resources.
- Minimize costs.
Technical requirements
- Dynamically scale based on game activity.
- Publish scoring data on a near real-time global leaderboard.
- Store game activity logs in structured files for future analysis.
- Use GPU processing to render graphics server-side for multi-platform support.
- Support eventual migration of legacy games to this new platform.
Executive statement
Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before.
This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality.
Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily. You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user's perspective. What should you do?
A. Create Request Latency and Error Rate as service level indicators.
B. Create CPU Utilization and Request Latency as service level indicators.
C. Create Server Uptime and Error Rate as service level indicators.
D. Create GKE CPU Utilization and Memory Utilization as service level indicators.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You are developing a globally scaled frontend for a legacy streaming backend data API. This API expects events in strict chronological order with no repeat data for proper processing. Which products should you deploy to ensure guaranteed-once FIFO (first-in, first-out) delivery of data?
A. Cloud Pub/Sub to Cloud DataFlow
B. Cloud Pub/Sub to Stackdriver
C. Cloud Pub/Sub to Cloud SQL
D. Cloud Pub/Sub alone
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You have an application that will run on Compute Engine. You need to design an architecture that takes into account a disaster recovery plan that requires your application to fail over to another region in case of a regional outage. What should you do?
A. Deploy the application on two Compute Engine instance groups, each in separate project and a different region. Use the first instance group to server traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
B. Deploy the application on a Compute Engine instance. Use the instance to serve traffic, and use the HTTP load balancing service to fail over to an instance on your premises in case of a disaster.
C. Deploy the application on two Compute Engine instances in the same project but in a different region.
Use the first instance to serve traffic, and use the HTTP load balancing service to fail over to the standby instance in case of a disaster.
D. Deploy the application on two Compute Engine instance groups, each in the same project but in a different region. Use the first instance group to serve traffic, and use the HTTP load balancing service to fail over to the standby instance group in case of a disaster.
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Case Study: 5 - Dress4win
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster.
Dress4Win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
- MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
- Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
- 40 Web Application servers providing micro-services based APIs and static content.
- Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
- 20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
- 3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
- Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
- iSCSI for VM hosts
- Fiber channel SAN - MySQL databases
- 1 PB total storage; 400 TB available
- NAS - image storage, logs, backups
- 100 TB total storage; 35 TB available
Business Requirements
- Build a reliable and reproducible environment with scaled parity of production.
- Improve security by defining and adhering to a set of security and Identity and Access
Management (IAM) best practices for cloud.
- Improve business agility and speed of innovation through rapid provisioning of new resources.
- Analyze and optimize architecture for performance in the cloud.
Technical Requirements
- Easily create non-production environment in the cloud.
- Implement an automation framework for provisioning resources in cloud.
- Implement a continuous deployment process for deploying applications to the on-premises
datacenter or cloud.
- Support failover of the production environment to cloud during an emergency.
- Encrypt data on the wire and at rest.
- Support multiple private connections between the production data center and cloud
environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
For this question, refer to the Dress4Win case study. Dress4Win is expected to grow to 10 times its size in 1 year with a corresponding growth in data and traffic that mirrors the existing patterns of usage. The CIO has set the target of migrating production infrastructure to the cloud within the next 6 months. How will you configure the solution to scale for this growth without making major application changes and still maximize the ROI?
A. Migrate RabbitMQ to Cloud Pub/Sub, Hadoop to BigQuery, and NAS to Compute Engine with Persistent Disk storage. Deploy Tomcat, and deploy Nginx using Deployment Manager.
B. Implement managed instance groups for the Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Cloud Storage.
C. Implement managed instance groups for Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Compute Engine with Persistent Disk storage.
D. Migrate the web application layer to App Engine, and MySQL to Cloud Datastore, and NAS to Cloud Storage. Deploy RabbitMQ, and deploy Hadoop servers using Deployment Manager.
正解：B
解説: (Topexam メンバーにのみ表示されます)