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Google Professional-Cloud-Architect認定試験は、クラウドコンピューティングの深い理解と複雑なクラウドソリューションの設計と管理能力が必要な厳しい試験です。しかし、この認定を取得することで、プロフェッショナルはキャリアを進め、収入を増やし、クラウドコンピューティングの専門知識を証明することができます。クラウドコンピューティングの専門家の需要が増える中、Google Professional-Cloud-Architect認定は、この分野でキャリアを進めたいプロフェッショナルにとって不可欠な資格になっています。

Google Professional-Cloud-Architectの認定は、GCPを扱う専門家にとって価値のある資格です。認定は、候補者がGCP上で安全かつスケーラブルで信頼性の高いソリューションを設計・開発・管理する知識とスキルを試験で検証します。認定は、業界で高く評価され、多くの組織が認めています。認定試験に合格することは、候補者のGCPに対する専門知識や、組織のニーズに応えるソリューションを設計・実装する能力を示す証明となります。

Google Professional-Cloud-Architect 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Designing and planning a cloud solution architecture: This section evaluates the skills of cloud architects in designing infrastructures that align with both business and technical requirements. It encompasses understanding business use cases, cost optimization, application support, external system integration, data movement, and making informed design decisions. Additionally, it involves creating migration plans, integrating solutions with existing systems, and anticipating future improvements based on evolving business needs and technological advancements.
トピック 2	Managing implementation: This section assesses project managers' abilities to oversee the successful deployment of cloud solutions. It involves advising development and operations teams, understanding application development, adhering to API best practices, utilizing testing frameworks, managing data and system migrations, and interacting with Google Cloud programmatically using tools like Cloud Shell and Cloud SDK.
トピック 3	Analyzing and optimizing technical and business processes: Aimed at business analysts, this section evaluates the analysis and definition of technical and business processes. It includes understanding the software development life cycle, continuous integration and deployment practices, troubleshooting methodologies, testing and validation, service provisioning, and disaster recovery planning. Furthermore, it involves stakeholder management, change management, team skill assessments, decision-making processes, customer success strategies, and cost optimization efforts.

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Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q189-Q194):

質問 # 189
An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data.
You want to help them find a solution that meets their needs.
What should you do?

A. Help them define their requirements and assess viable logging tools
B. Help them upgrade their current tool to take advantage of any new features
C. Direct them to download and install the Google StackDriver logging agent
D. Send them a list of online resources about logging best practices

正解：C

解説：
The Stackdriver Logging agent streams logs from your VM instances and from selected third party software packages to Stackdriver Logging. Using the agent is optional but we recommend it. The agent runs under both Linux and Microsoft Windows.
Note: Stackdriver Logging allows you to store, search, analyze, monitor, and alert on log data and events from Google Cloud Platform and Amazon Web Services (AWS). Our API also allows ingestion of any custom log data from any source. Stackdriver Logging is a fully managed service that performs at scale and can ingest application and system log data from thousands of VMs. Even better, you can analyze all that log data in real time.
Reference: https://cloud.google.com/logging/docs/agent/installation
https://medium.com/google-cloud/hidden-super-powers-of-stackdriver-logging-ca110dae7e74

質問 # 190
The application reliability team at your company has added a debug feature to their backend service to send all server events to Google Cloud Storage for eventual analysis. The event records are at least 50 KB and at most 15 MB and are expected to peak at 3,000 events per second. You want to minimize data loss.
Which process should you implement?

A. * Batch every 10,000 events with a single manifest file for metadata.
* Compress event files and manifest file into a single archive file.
* Name files using serverName-EventSequence.
* Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket. Otherwise, save the single archive file to existing bucket.
B. * Append metadata to file body.
* Compress individual files.
* Name files with serverName-Timestamp.
* Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket. Otherwise, save files to existing bucket
C. * Compress individual files.
* Name files with serverName-EventSequence.
* Save files to one bucket
* Set custom metadata headers for each object after saving.
D. * Append metadata to file body.
* Compress individual files.
* Name files with a random prefix pattern.
* Save files to one bucket

正解：D

解説：
Reference:
In order to maintain a high request rate, avoid using sequential names. Using completely random object names will give you the best load distribution. Randomness after a common prefix is effective under the prefix https://cloud.google.com/storage/docs/request-rate

質問 # 191
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?

A. Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts.
B. Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.
C. Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Launcher.
D. Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher.

正解：B

解説：
Topic 7, TerramEarth Case 2
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.

質問 # 192
You have deployed several instances on Compute Engine. As a security requirement, instances cannot have a public IP address. There is no VPN connection between Google Cloud and your office, and you need to connect via SSH into a specific machine without violating the security requirements. What should you do?

A. Configure Identity-Aware Proxy (IAP) for the instance and ensure that you have the role of IAP- secured Tunnel User.
Use the gcloud command line tool to ssh into the instance.
B. Add all instances to an unmanaged instance group.
Configure TCP Proxy Load Balancing with the instance group as a backend.
Connect to the instance using the TCP Proxy IP.
C. Create a bastion host in the network to SSH into the bastion host from your office location.
From the bastion host, SSH into the desired instance.
D. Configure Cloud NAT on the subnet where the instance is hosted.
Create an SSH connection to the Cloud NAT IP address to reach the instance.

正解：A

解説：
https://cloud.google.com/iap/docs/using-tcp-forwarding#tunneling_with_ssh

質問 # 193
You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore.
What should you do?

A. Upload the configuration file the App Engine's default Cloud Storage bucket, and have App Engine detect the new indexes
B. Point gcloud datastore create-indexes to your configuration file
C. In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file
D. Create an HTTP request to the built-in python module to send the index configuration file to your application

正解：C

質問 # 194
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