Professional-Cloud-Architect資格専門知識を選択し、Google Certified Professional

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Google Professional-Cloud-Architect試験は、候補者がGoogle Cloud Platformソリューションを設計および実装する能力をテストする認定試験です。試験は、Google Cloud Platformを使用してクラウドソリューションを設計、開発、管理する責任があるプロフェッショナル向けに設計されています。試験は、クラウドアーキテクチャの専門知識を検証し、クラウドコンピューティングのキャリアを進めたい個人にも適しています。

Professional-Cloud-Architect試験の資格を得るには、候補者はクラウドアーキテクチャで少なくとも3年の経験があり、GCPサービスの深い理解を深める必要があります。また、クラウドの設計パターンやベストプラクティスに精通しているだけでなく、セキュリティとコンプライアンスの要件の経験があるはずです。この試験は、実際のシナリオで候補者をテストするように設計されており、GCPでソリューションを設計、開発、管理する能力を検証することを目的としています。

Professional-Cloud-Architect資格専門知識

認定されたProfessional-Cloud-Architect資格専門知識を信頼するのが最も簡単なGoogle Certified Professional - Cloud Architect (GCP)方法です

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Google Professional-Cloud-Architect試験は、Google Cloudプラットフォームでソリューションの設計、開発、管理の専門知識を実証したい専門家の認定です。クラウドアーキテクチャの経験があり、スキルセットの拡大を検討している個人向けに設計されています。試験では、GCPのソリューションを設計および管理する能力と、クラウドアーキテクチャの原則の理解について候補者をテストします。

Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q28-Q33):

質問 # 28
A news teed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed. What is the most likely cause of this problem?

A. The HTTP Expires header needs to be set to -1 to stop caching.
B. The URL of the API needs to be modified to prevent caching.
C. The session variable is local to just a single instance.
D. The session variable is being overwritten in Cloud Datastore.

正解：A

質問 # 29
You are using Cloud CDN to deliver static HTTP(S) website content hosted on a Compute Engine instance group. You want to improve the cache hit ratio.
What should you do?

A. Make sure the HTTP(S) header "Cache-Region" points to the closest region of your users.
B. Shorten the expiration time of the cached objects.
C. Customize the cache keys to omit the protocol from the key.
D. Replicate the static content in a Cloud Storage bucket. Point CloudCDN toward a load balancer on that bucket.

正解：C

解説：
Explanation/Reference:
Reference https://cloud.google.com/cdn/docs/best-
practices#using_custom_cache_keys_to_improve_cache_hit_ratio

質問 # 30
Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize operations. They do not have any existing code for this analysis, so they are exploring all their options. These options include a mix of batch and stream processing, as they are running some hourly jobs and live-processing some data as it comes in. Which technology should they use for this?

A. Google Cloud Dataflow
B. Google Cloud Dataproc
C. Google Compute Engine with Google BigQuery
D. Google Container Engine with Bigtable

正解：A

解説：
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness -- no more complex workarounds or compromises needed.
References: https://cloud.google.com/dataflow/

質問 # 31
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 update. Select the Opportunistic update mode.
B. Start a new rolling update. Select the Proactive update mode.
C. Start a new rolling replace operation.
D. Start a new rolling restart operation.

正解：A

質問 # 32
For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
B. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.
C. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
D. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.

正解：C

解説：
Topic 7, Mountkrik Games Case 2
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements
Increase to a global footprint.
Improve uptime - downtime is loss of players.
Increase efficiency of the cloud resources we use.
Reduce latency to all customers.
Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.
Connect to a transactional database service to manage user profiles and game state.
Store game activity in a timeseries database service for future analysis.
As the system scales, ensure that data is not lost due to processing backlogs.
Run hardened Linux distro.
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.

質問 # 33
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