Professional-Cloud-Architect日本語受験攻略、Professional-Cloud-Architect合格対策

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Professional-Cloud-Architect学習教材を購入すると、Professional-Cloud-Architectテストにスムーズかつ簡単に合格します。プロの専門家チームを強化してProfessional-Cloud-Architectトレーニング資料を熱心に編成および編集し、販売前後のサービス、24時間のオンラインカスタマーサービス、払い戻しサービスなどの素晴らしいサービスを提供します。 Professional-Cloud-Architectの実際のクイズでは、3つのバージョンとさまざまな機能が強化され、包括的かつ効率的に学習できます。学習教材の学習は時間と労力をほとんど必要とせず、頻繁に更新されます。質問：Google Certified Professional - Cloud Architect (GCP)の詳細については、次のように製品の紹介をご覧ください。

Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Managing implementation	12.5%	- Guiding development and deployment teams 1. CI/CD pipelines and automation 2. Application and infrastructure deployment strategies - Testing and validating solutions 1. Disaster recovery and failover testing 2. Functional, performance, and security testing - Using Google Cloud tools and APIs programmatically 1. Cloud SDK, Cloud Shell, and Cloud Code 2. Infrastructure as Code (Terraform, Deployment Manager)
Managing and provisioning solution infrastructure	15%	- Provisioning and managing compute resources 1. Auto-scaling and resource management 2. VMs, GKE, App Engine, Cloud Run deployment and management - Managing storage and data services 1. Database deployment, replication, and backup 2. Cloud Storage, Filestore, Persistent Disk management - Configuring network topologies and connectivity 1. Load balancing and CDN configuration 2. VPC design, peering, and hybrid connectivity
Designing for security and compliance	17.5%	- Implementing identity and access management 1. Organizational policies and resource hierarchy 2. IAM roles, permissions, and service accounts - Securing data and communications 1. Encryption at rest and in transit 2. Key management and secret security - Enforcing compliance and governance 1. VPC Service Controls and security perimeters 2. Security controls, auditing, and logging
Ensuring solution and operations excellence	12.5%	- Planning for continuous improvement 1. Feedback loops and optimization cycles 2. Technology updates and architecture evolution - Applying Well-Architected Framework principles 1. Operational excellence, reliability, security, cost, and performance pillars - Implementing monitoring, logging, and observability 1. Alerting, dashboarding, and root cause analysis 2. Cloud Monitoring, Logging, and Error Reporting
Designing and planning a cloud solution architecture	25%	- Planning cloud migration and hybrid/multi-cloud environments 1. Migration planning and execution 2. Interoperability and integration design 3. Data transfer and synchronization - Evaluating and selecting Google Cloud services 1. Serverless and managed service adoption 2. Compute, storage, database, and networking service selection - Designing cloud solution infrastructure aligned with business requirements 1. Scalability, high availability, and fault tolerance design 2. Compliance and regulatory requirements consideration 3. Cost optimization strategies
Analyzing and optimizing technical and business processes	15%	- Improving reliability and performance 1. SLO, SLA, and error budgeting 2. Latency reduction and throughput optimization - Streamlining operational workflows 1. Stakeholder communication and alignment 2. Change management and incident response - Optimizing cost and resource efficiency 1. Resource right-sizing and usage monitoring 2. Committed use discounts and pricing models

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Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q342-Q347):

質問 # 342
You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading.
Where should you store the data?

A. Google BigQuery
B. Google Cloud Bigtable
C. Google Cloud Storage
D. Google Cloud SQL

正解：B

解説：
Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real- time access and analytics workloads.
Good for:
* Low-latency read/write access
* High-throughput analytics
* Native time series support
Common workloads:
* IoT, finance, adtech
* Personalization, recommendations
* Monitoring
* Geospatial datasets
* Graphs
References: https://cloud.google.com/storage-options/

質問 # 343
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 Puppet to create the managed instance group and install the OS package dependencies.
B. Use Deployment Manager to create the managed instance group and Ansible to install the OS package dependencies.
C. Use Terraform to create the managed instance group and a startup script to install the OS package dependencies.
D. Create a custom VM image with all OS package dependencies. Use Deployment Manager to create the managed instance group with the VM image.

正解：D

解説：
Explanation
"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_i

質問 # 344
For this question, refer to the Mountkirk Games case study. You are in charge of the new Game Backend Platform architecture. The game communicates with the backend over a REST API.
You want to follow Google-recommended practices. How should you design the backend?

A. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.
B. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.
C. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.
D. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.

正解：D

質問 # 345
You are designing a Data Warehouse on Google Cloud and want to store sensitive data in BigQuery. Your company requires you to generate encryption keys outside of Google Cloud. You need to implement a solution. What should you do?

A. Generate a new key in Cloud Key Management Service (Cloud KMS). Create a dataset in BigQuery using the customer-managed key option and select the created key
B. Import a key in Cloud KMS. Store all data in Cloud Storage using the customer-managed key option and select the created key. Set up a Dataflow pipeline to decrypt the data and to store it in a new BigQuery dataset.
C. Generate a new key in Cloud Key Management Service (Cloud KMS). Store all data in Cloud Storage using the customer-managed key option and select the created key. Set up a Dataflow pipeline to decrypt the data and to store it in a BigQuery dataset.
D. Import a key in Cloud KMS. Create a dataset in BigQuery using the customer-supplied key option and select the created key.

正解：D

解説：
Explanation
https://cloud.google.com/bigquery/docs/customer-managed-encryption

質問 # 346
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also to reliably deploy new versions of the application. Which set of steps should you take?

A. Perform the following:
1) Create a managed instance group with n1-standard-1 type machines.
2) Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3) Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
B. Perform the following:
1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.
2) Build a Docker image from the master branch will all of the dependencies, and tag it with "latest".
3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to "Always".Restart the pods to automatically deploy new production releases.
C. Perform the following:
1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.
2) Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3) Create a Kubernetes Deployment with the imagePullPolicy set to "IfNotPresent" in the staging namespace, and then promote it to the production namespace after testing.
D. Perform the following:
1) Create a managed instance group with f1-micro type machines.
2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3) Restart the instances to automatically deploy new production releases.

正解：B

解説：
https://cloud.google.com/compute/docs/instance-templates

質問 # 347
......

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Professional-Cloud-Architect日本語受験攻略、Professional-Cloud-Architect合格対策

Google Professional-Cloud-Architect Exam Syllabus Topics:

高品質Google Professional-Cloud-Architect日本語受験攻略 は主要材料 & 無料PDFProfessional-Cloud-Architect合格対策

Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q342-Q347):

高品質Google Professional-Cloud-Architect日本語受験攻略は主要材料 & 無料PDFProfessional-Cloud-Architect合格対策