究極のガイド準備Professional-Cloud-Architect認証試験Google Cloud Certifiedは2026年更新
リアルProfessional-Cloud-Architect問題集でGoogle正確なアンサーは最新問題は2026年更新
認定されたProfessional-Cloud-Architectになることの利点は多数あります。第一に、クラウドアーキテクチャとGCPにおける高レベルの専門知識を示しています。第二に、組織がクラウドソリューションを設計および管理するために必要なスキルを持つ専門家を特定するのに役立ちます。最後に、GCPの最新の開発を最新の状態に保つために、認定専門家とリソースのコミュニティへのアクセスを提供します。
質問 # 168
For this question, refer to the Helicopter Racing League (HRL) case study. HRL is looking for a cost-effective approach for storing their race data such as telemetry. They want to keep all historical records, train models using only the previous season's data, and plan for data growth in terms of volume and information collected.
You need to propose a data solution. Considering HRL business requirements and the goals expressed by CEO S.
Hawke, what should you do?
- A. Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
- B. Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
- C. Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.
- D. Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.
正解：A
解説：
Reference: https://cloud.google.com/bigquery/public-data
質問 # 169
Your company has an application running on Compute Engine mat allows users to play their favorite music. There are a fixed number of instances Files are stored in Cloud Storage and data is streamed directly to users. Users are reporting that they sometimes need to attempt to play popular songs multiple times before they are successful. You need to improve the performance of the application. What should you do?
- A. 1. Create a Cloud Filestore NFS volume and attach it to the backend Compute Engine instances
2. Download popular songs in Cloud Filestore
3. Serve music Wes directly from the backend Compute Engine instance - B. 1. Create a managed instance group with Compute Engine instances
2. Create a global toad balancer and configure ii with two backbends
* Managed instance group
* Cloud Storage bucket
3. Enable Cloud CDN on the bucket backend - C. 1. Mount the Cloud Storage bucket using gcsfuse on all backend Compute Engine instances
2. Serve muse files directly from the backend Compute Engine instance - D. 1. Copy popular songs into CloudSQL as a blob
2. Update application code to retrieve data from CloudSQL when Cloud Storage is overloaded
正解：B
質問 # 170
Case Study: 11 - TerramEarth
Company overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. They currently have over 500 dealers and service centers in 100 countries.
Their mission is to build products that make their customers more productive.
Solution concept
There are 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth.
Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base.
Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in Google Cloud and allows access to stock management and analytics.
Business requirements
- Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
- Decrease cloud operational costs and adapt to seasonality.
- Increase speed and reliability of development workflow.
- Allow remote developers to be productive without compromising code or data security.
- Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements
- Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
- Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
- Allow developers to run experiments without compromising security and governance requirements.
- Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
- Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
- Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in- class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
For this question, refer to the Terram Earth case study. Terram Earth receives daily data in the Cloud using network interconnects with private on-premises data centers.A subset of the data is transmitted and processed in real time and the rest daily, when the vehicles return to home base.
You have been asked to prepare a complete solution for the ingestion and management of this data, which must be both fully stored and aggregated for analytics with Bigquery. Which of the following actions do you think is the best solution (pick 2)?
- A. Real-time data is streamed to BigQuery, and each day a job creates all the required aggregate processing
- B. Real-time data is sent via Pub / Sub and processed by Dataflow that stores data in Cloud Storage and computes the aggregates for BigQuery.
- C. The Daily Sensor data is uploaded to Cloud Storage with parallel composite uploads and at the end with a Cloud Storage Trigger a Dataflow procedure is activated
- D. Daily Sensor data is loaded quickly with BigQuery Data Transfer Service and processed on demand via job
正解：B、C
質問 # 171
Case Study: 6 - TerramEarth
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week.
Support the dealer network with more data on how their customers use their equipment to better
position new products and services
Have the ability to partner with different companies - especially with seed and fertilizer suppliers
in the fast-growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east
coast.
Create a backup strategy.
Increase security of data transfer from equipment to the datacenter.
Improve data in the data warehouse.
Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?
- A. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
- B. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
- C. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
- D. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.
正解：A
質問 # 172
For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost.
Which two actions should you take?
- A. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Coldline", and Action:
"Delete". - B. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Standard", and Action: "Set to Coldline", and create a second GCS life-cycle rule with Age: "365", Storage Class: "Nearline", and Action:
"Delete". - C. Create a Cloud Storage lifecycle rule with Age: "90", Storage Class: "Standard", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Nearline", and Action:
"Set to Coldline". - D. Create a Cloud Storage lifecycle rule with Age: "30", Storage Class: "Coldline", and Action: "Set to Nearline", and create a second GCS life-cycle rule with Age: "91", Storage Class: "Coldline", and Action: "Set to Nearline".
正解：A
質問 # 173
For this question, refer to the JencoMart case study
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly. What three steps should you take to diagnose the problem? Choose 3 answers
- A. Delete the virtual machine (VM) and disks and create a new one.
- B. Check inbound firewall rules for the network the machine is connected to.
- C. Take a snapshot of the disk and connect to a new machine to investigate.
- D. Delete the instance, attach the disk to a new VM, and investigate.
- E. Connect the machine to another network with very simple firewall rules and investigate.
- F. Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate.
正解：A、D、E
質問 # 174
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface.
How should you store the data to optimize it for ease of analysis?
- A. Stream data into Google Cloud Datastore.
- B. Insert data into Google Cloud SQL.
- C. Put flat files into Google Cloud Storage.
- D. Load data into Google BigQuery.
正解：D
解説：
Google Big Query is for multi peta byte storage , HA(High availability) which means 24 hours, SQL interface .
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/
質問 # 175
You are moving an application that uses MySQL from on-premises to Google Cloud. The application will run on Compute Engine and will use Cloud SQL. You want to cut over to the Compute Engine deployment of the application with minimal downtime and no data loss to your customers. You want to migrate the application with minimal modification. You also need to determine the cutover strategy. What should you do?
- A. 1. Set up Cloud VPN to provide private network connectivity between the Compute Engine application and the on-premises MySQL server.
2. Stop the on-premises application.
3. Start the Compute Engine application, configured to read and write to the on-premises MySQL server.
4. Create the replication configuration in Cloud SQL.
5. Configure the source database server to accept connections from the Cloud SQL replica.
6. Finalize the Cloud SQL replica configuration.
7. When replication has been completed, stop the Compute Engine application.
8. Promote the Cloud SQL replica to a standalone instance.
9. Restart the Compute Engine application, configured to read and write to the Cloud SQL standalone instance. - B. 1. Stop the on-premises application.
2. Create a mysqldump of the on-premises MySQL server.
3. Upload the dump to a Cloud Storage bucket.
4. Import the dump into Cloud SQL.
5. Start the application on Compute Engine. - C. 1. Set up Cloud VPN to provide private network connectivity between the Compute Engine application and the on-premises MySQL server.
2. Stop the on-premises application.
3. Create a mysqldump of the on-premises MySQL server.
4. Upload the dump to a Cloud Storage bucket.
5. Import the dump into Cloud SQL.
6. Modify the source code of the application to write queries to both databases and read from its local database.
7. Start the Compute Engine application.
8. Stop the on-premises application. - D. 1. Set up Cloud SQL proxy and MySQL proxy.
2. Create a mysqldump of the on-premises MySQL server.
3. Upload the dump to a Cloud Storage bucket.
4. Import the dump into Cloud SQL.
5. Stop the on-premises application.
6. Start the Compute Engine application.
正解：A
質問 # 176
Case Study: 10 - EHR Healthcare
Company overview
EHR Healthcare is a leading provider of electronic health record software to the medical industry.
EHR Healthcare provides their software as a service to multi-national medical offices, hospitals, and insurance providers.
Solution concept
Due to rapid changes in the healthcare and insurance industry, EHR Healthcare's business has been growing exponentially year over year. They need to be able to scale their environment, adapt their disaster recovery plan, and roll out new continuous deployment capabilities to update their software at a fast pace. Google Cloud has been chosen to replace their current colocation facilities.
Existing technical environment
EHR's software is currently hosted in multiple colocation facilities. The lease on one of the data centers is about to expire.
Customer-facing applications are web-based, and many have recently been containerized to run on a group of Kubernetes clusters. Data is stored in a mixture of relational and NoSQL databases (MySQL, MS SQL Server, Redis, and MongoDB).
EHR is hosting several legacy file- and API-based integrations with insurance providers on- premises. These systems are scheduled to be replaced over the next several years. There is no plan to upgrade or move these systems at the current time.
Users are managed via Microsoft Active Directory. Monitoring is currently being done via various open source tools. Alerts are sent via email and are often ignored.
Business requirements
* On-board new insurance providers as quickly as possible.
* Provide a minimum 99.9% availability for all customer-facing systems.
* Provide centralized visibility and proactive action on system performance and usage.
* Increase ability to provide insights into healthcare trends.
* Reduce latency to all customers.
* Maintain regulatory compliance.
* Decrease infrastructure administration costs.
* Make predictions and generate reports on industry trends based on provider data.
Technical requirements
* Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers.
* Provide a consistent way to manage customer-facing applications that are container-based.
* Provide a secure and high-performance connection between on-premises systems and Google Cloud.
* Provide consistent logging, log retention, monitoring, and alerting capabilities.
* Maintain and manage multiple container-based environments.
* Dynamically scale and provision new environments.
* Create interfaces to ingest and process data from new providers.
Executive statement
Our on-premises strategy has worked for years but has required a major investment of time and money in training our team on distinctly different systems, managing similar but separate environments, and responding to outages. Many of these outages have been a result of misconfigured systems, inadequate capacity to manage spikes in traffic, and inconsistent monitoring practices. We want to use Google Cloud to leverage a scalable, resilient platform that can span multiple environments seamlessly and provide a consistent and stable user experience that positions us for future growth.
For this question, refer to the EHR Healthcare case study. You are responsible for designing the Google Cloud network architecture for Google Kubernetes Engine. You want to follow Google best practices. Considering the EHR Healthcare business and technical requirements, what should you do to reduce the attack surface?
- A. Use a public cluster with firewall rules and Virtual Private Cloud (VPC) routes.
- B. Use a public cluster with master authorized networks enabled and firewall rules.
- C. Use a private cluster with a public endpoint with master authorized networks configured.
- D. Use a private cluster with a private endpoint with master authorized networks configured.
正解：D
解説：
Public endpoint access disabled is the most secure option as it prevents all internet access to the control plane. This is a good choice if you have configured your on-premises network to connect to Google Cloud using Cloud Interconnect (EHR has enabled this) or Cloud VPN.
If you disable public endpoint access, then you must configure authorized networks for the private endpoint. If you don't do this, you can only connect to the private endpoint from cluster nodes or VMs in the same subnet as the cluster.
Public endpoint access enabled, authorized networks enabled: This is a good choice if you need to administer the cluster from source networks that are not connected to your cluster's VPC network using Cloud Interconnect or Cloud VPN (but EHR is already using interconnect).
Reference- https://cloud.google.com/kubernetes-engine/docs/concepts/private-cluster-concept
質問 # 177
For this question, refer to the JencoMart case study
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly. What three steps should you take to diagnose the problem?
Choose 3 answers
- A. Take a snapshot of the disk and connect to a new machine to investigate.
- B. Delete the virtual machine (VM) and disks and create a new one.
- C. Check inbound firewall rules for the network the machine is connected to.
- D. Delete the instance, attach the disk to a new VM, and investigate.
- E. Connect the machine to another network with very simple firewall rules and investigate.
- F. Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate.
正解：A、C、E
解説：
Explanation
https://cloud.google.com/compute/docs/troubleshooting/troubleshooting-ssh D: Handling "Unable to connect on port 22" error message Possible causes include:
There is no firewall rule allowing SSH access on the port. SSH access on port 22 is enabled on all Compute Engine instances by default. If you have disabled access, SSH from the Browser will not work. If you run sshd on a port other than 22, you need to enable the access to that port with a custom firewall rule.
The firewall rule allowing SSH access is enabled, but is not configured to allow connections from GCP Console services. Source IP addresses for browser-based SSH sessions are dynamically allocated by GCP Console and can vary from session to session.
References:
https://cloud.google.com/compute/docs/ssh-in-browser
https://cloud.google.com/compute/docs/ssh-in-browser
質問 # 178
You have developed a non-critical update to your application that is running in a managed instance group, and have created a new instance template with the update that you want to release. To prevent any possible impact to the application, you don't want to update any running instances. You want any new instances that are created by the managed instance group to contain the new update. What should you do?
- A. Start a new rolling replace operation.
- B. Start a new rolling update. Select the Opportunistic update mode.
- C. Start a new rolling update. Select the Proactive update mode.
- D. Start a new rolling restart operation.
正解：B
解説：
Explanation
In certain scenarios, an opportunistic update is useful because you don't want to cause instability to the system if it can be avoided. For example, if you have a non-critical update that can be applied as necessary without any urgency and you have a MIG that is actively being autoscaled, perform an opportunistic update so that Compute Engine does not actively tear down your existing instances to apply the update. When resizing down, the autoscaler preferentially terminates instances with the old template as well as instances that are not yet in a RUNNING state.
質問 # 179
You need to develop procedures to verify resilience of disaster recovery for remote recovery using GCP.
Your production environment is hosted on-premises. You need to establish a secure, redundant connection between your on-premises network and the GCP network.
What should you do?
- A. Verify that the Transfer Appliance can replicate files to GCP. Verify that Cloud VPN can establish a secure connection between your networks if the Transfer Appliance fails.
- B. Verify that Dedicated Interconnect can replicate files to GCP. Verify that Cloud VPN can establish a secure connection between your networks if Dedicated Interconnect fails.
- C. Verify that Dedicated Interconnect can replicate files to GCP. Verify that direct peering can establish a secure connection between your networks if Dedicated Interconnect fails.
- D. Verify that the Transfer Appliance can replicate files to GCP. Verify that direct peering can establish a secure connection between your networks if the Transfer Appliance fails.
正解：B
質問 # 180
Your organization requires that metrics from all applications be retained for 5 years for future analysis in possible legal proceedings. Which approach should you use?
- A. Configure Stackdriver Monitoring for all Projects, and export to BigQuery.
- B. Grant the security team access to the logs in each Project.
- C. Configure Stackdriver Monitoring for all Projects with the default retention policies.
- D. Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage.
正解：D
解説：
For archival purposes, Customer should use Cloud Storage. BigQuery is a datawarehouse, and could eventually import data from Cloud Storage if necessary.
References:
https://cloud.google.com/monitoring/api/v3/metrics
https://cloud.google.com/stackdriver/
質問 # 181
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.
Mountkirk Games wants you to secure the connectivity from the new gaming application platform to Google Cloud. You want to streamline the process and follow Google-recommended practices.
What should you do?
- A. Configure HashiCorp Vault on Compute Engine, and use customer managed encryption keys and Cloud Key Management Service (Cloud KMS) to manage the encryption keys.
Configure these Secrets to be used by the application platform. - B. Configure Workload Identity and service accounts to be used by the application platform.
- C. Configure Kubernetes Secrets to store the secret, enable Application-Layer Secrets Encryption, and use Cloud Key Management Service (Cloud KMS) to manage the encryption keys.
Configure these Secrets to be used by the application platform. - D. Use Kubernetes Secrets, which are obfuscated by default.
Configure these Secrets to be used by the application platform.
正解：B
解説：
https://cloud.google.com/kubernetes-engine/docs/how-to/workload-identity Workload Identity is the recommended way to access Google Cloud services from applications running within GKE due to its improved security properties and manageability. For information about alternative ways to access Google Cloud APIs from GKE, refer to the alternatives section below.
質問 # 182
For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)
- A. Begin packaging their game backend artifacts in container images and running them on Google Kubernetes Engine to improve the ability to scale up or down based on game activity.
- B. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
- C. Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.
- D. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
- E. Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.
正解：D、E
質問 # 183
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. You want to ensure that your on-premises architecture meets business requirements before you migrate your solution.
What change in the on-premises architecture should you make?
- A. Replace RabbitMQ with Google Pub/Sub.
- B. Containerize the micro services and host them in Google Kubernetes Engine.
- C. Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.
- D. Resize compute resources to match predefined Compute Engine machine types.
正解：B
解説：
Containerizing the existing applications ensures efficient use of resources. This activity the business requirement "optimize architecture for performance in the cloud". As a precursor to Cloud migration, you could convert the microservices to containers and host them on GKE on- prem: https://cloud.google.com/anthos/gke/docs/on-prem/overview which also makes it very easy for you to migrate to Cloud. GKE on-prem is hybrid cloud software that brings Google Kubernetes Engine (GKE) to on-premises data centres. With GKE on-prem, you can create, manage, and upgrade Kubernetes clusters in your on-premises environment.
質問 # 184
You have broken down a legacy monolithic application into a few containerized RESTful microservices. You want to run those microservices on Cloud Run. You also want to make sure the services are highly available with low latency to your customers. What should you do?
- A. Deploy Cloud Run services to multiple availability zones. Create a TCP/IP global load balancer. Add the Cloud Run Endpoints to its backend service.
- B. Deploy Cloud Run services to multiple regions Create serverless network endpoint groups pointing to the services. Add the serverless NE Gs to a backend service that is used by a global HTIP(S) Load Balancing instance.
- C. Deploy Cloud Run services to multiple availability zones. Create Cloud Endpoints that point to the services. Create a global HTIP(S) Load Balancing instance and attach the Cloud Endpoints to its backend.
- D. Cloud Run services to multiple regions. In Cloud DNS, create a latency-based DNS name that points to the services.
正解：B
解説：
https://cloud.google.com/run/docs/multiple-regions
質問 # 185
For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?
- A. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
- B. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
- C. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
- D. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
正解：A
質問 # 186
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