Professional-Cloud-Architect試験の準備方法｜便利なProfessional-Cloud-Architectオンライン試験試験｜実際的なGoogle Certified Professional - Cloud Architect (GCP)復習問題集

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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Topic 1: Design and plan a cloud solution architecture	- Design Google Cloud solutions based on business requirements - Plan security and compliance requirements - Design for reliability, scalability, and availability
Topic 2: Design for security and compliance	- Implement identity and access management (IAM) - Configure data protection and encryption
Topic 3: Manage implementations of cloud architecture	- Ensure solution reliability and operational excellence - Manage deployment and production systems
Topic 4: Manage and provision the solution infrastructure	- Provision compute, storage, and network resources - Automate infrastructure deployment
Topic 5: Analyze and optimize technical and business processes	- Monitor, log, and optimize system performance - Optimize cost and resource usage

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Professional-Cloud-Architect復習問題集 & Professional-Cloud-Architect日本語

Professional-Cloud-Architect認定試験はずっと人気があるのです。最近IT試験を受けて認証資格を取ることは一層重要になりました。たとえばGoogle、IBM、Cisco、VMware、SAPなどのいろいろな試験は今では全部非常に重要な試験です。より多くの人々は複数の資格を取得するために多くのProfessional-Cloud-Architect試験を受験したいと思っています。もちろん、このようにすればあなたがすごい技能を身につけていることが証明されることができます。しかし、仕事しながら試験の準備をすることはもともと大変で、複数の試験を受験すれば非常に多くの時間が必要です。いまこのようなことで悩んいるのでしょうか。それは問題ではないですよ。CertShikenあなたを時間を節約させことができますから。CertShikenのさまざまなIT試験の問題集はあなたを受験したい任意の試験に合格させることができます。Professional-Cloud-Architect認定試験などの様々な認定試験で、受験したいなら躊躇わずに申し込んでください。心配する必要はないです。

Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q227-Q232):

質問 # 227
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 Server Uptime and Error Rate as service level indicators.
B. Create CPU Utilization and Request Latency as service level indicators.
C. Create Request Latency and Error Rate as service level indicators.
D. Create GKE CPU Utilization and Memory Utilization as service level indicators.

正解：C

解説：
Topic 10, Helicopter Racing League
Company Overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
Existing content is stored in an object storage service on their existing public cloud provider.
Video encoding and transcoding is performed on VMs created for each job.
Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.
Business Requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
Support ability to expose the predictive models to partners.
Increase predictive capabilities during and before races:
* Race results
* Mechanical failures
* Crowd sentiment
Increase telemetry and create additional insights.
Measure fan engagement with new predictions.
Enhance global availability and quality of the broadcasts.
Increase the number of concurrent viewers.
Minimize operational complexity.
Ensure compliance with regulations.
Create a merchandising revenue stream.
Technical Requirements
Maintain or increase prediction throughput and accuracy.
Reduce viewer latency.
Increase transcoding performance.
Create real-time analytics of viewer consumption patterns and engagement.
Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.

質問 # 228
You want to automate the creation of a managed instance group and a startup script to install the OS package dependencies. You want to minimize the startup time for VMs in the instance group.
What should you do?

A. Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.
B. Use Puppet to create the managed instance group and install the OS package dependencies.
C. Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.
D. Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.

正解：C

解説：
Reference:
"Custom images are more deterministic and start more quickly than instances with startup scripts. However, startup scripts are more flexible and let you update the apps and settings in your instances more easily." https://cloud.google.com/compute/docs/instance-templates/create-instance-templates#using_custom_or_public_images_in_your_instance_templates

質問 # 229
Your web application must comply with the requirements of the European Union's General Data Protection Regulation (GDPR). You are responsible for the technical architecture of your web application. What should you do?

A. Define a design for the security of data in your web application that meets GDPR requirements.
B. Enable the relevant GDPR compliance setting within the GCPConsole for each of the services in use within your application.
C. Ensure that Cloud Security Scanner is part of your test planning strategy in order to pick up any compliance gaps.
D. Ensure that your web application only uses native features and services of Google Cloud Platform, because Google already has various certifications and provides "pass-on" compliance when you use native features.

正解：A

解説：
Explanation/Reference: https://www.mobiloud.com/blog/gdpr-compliant-mobile-app/

質問 # 230
Your applications will be writing their logs to BigQuery for analysis. Each application should have its own table. Any logs older than 45 days should be removed. You want to optimize storage and follow Google-recommended practices. What should you do?

A. Configure the expiration time for your tables at 45 days
B. Rely on BigQuery's default behavior to prune application logs older than 45 days
C. Create a script that uses the BigQuery command line tool (bq) to remove records older than 45 days
D. Make the tables time-partitioned, and configure the partition expiration at 45 days

正解：D

解説：
https://cloud.google.com/bigquery/docs/best-practices-storage

質問 # 231
Your company is using BigQuery as its enterprise data warehouse. Data is distributed over several Google Cloud projects. All queries on BigQuery need to be billed on a single project. You want to make sure that no query costs are incurred on the projects that contain the data. Users should be able to query the datasets, but not edit them.
How should you configure users' access roles?

A. Add all users to a group. Grant the group the role of BigQuery user on the billing project and BigQuery dataViewer on the projects that contain the data.
B. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery jobUser on the projects that contain the data.
C. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery user on the projects that contain the data.
D. Add all users to a group. Grant the group the roles of BigQuery jobUser on the billing project and BigQuery dataViewer on the projects that contain the data.

正解：A

解説：
Reference: https://cloud.google.com/bigquery/docs/running-queries

質問 # 232
......

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