Professional-Cloud-Architect認定試験トレーリング & Professional-Cloud-Architect専門知識

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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing for security and compliance17.5%- Enforcing compliance and governance
  • 1. Security controls, auditing, and logging
  • 2. VPC Service Controls and security perimeters
- Implementing identity and access management
  • 1. IAM roles, permissions, and service accounts
  • 2. Organizational policies and resource hierarchy
- Securing data and communications
  • 1. Key management and secret security
  • 2. Encryption at rest and in transit
Topic 2: Managing implementation12.5%- 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)
- Guiding development and deployment teams
  • 1. CI/CD pipelines and automation
  • 2. Application and infrastructure deployment strategies
Topic 3: Designing and planning a cloud solution architecture25%- Evaluating and selecting Google Cloud services
  • 1. Serverless and managed service adoption
  • 2. Compute, storage, database, and networking service selection
- Planning cloud migration and hybrid/multi-cloud environments
  • 1. Migration planning and execution
  • 2. Interoperability and integration design
  • 3. Data transfer and synchronization
- Designing cloud solution infrastructure aligned with business requirements
  • 1. Cost optimization strategies
  • 2. Scalability, high availability, and fault tolerance design
  • 3. Compliance and regulatory requirements consideration
Topic 4: Managing and provisioning solution infrastructure15%- Configuring network topologies and connectivity
  • 1. VPC design, peering, and hybrid connectivity
  • 2. Load balancing and CDN configuration
- Managing storage and data services
  • 1. Database deployment, replication, and backup
  • 2. Cloud Storage, Filestore, Persistent Disk management
- Provisioning and managing compute resources
  • 1. VMs, GKE, App Engine, Cloud Run deployment and management
  • 2. Auto-scaling and resource management
Topic 5: Ensuring solution and operations excellence12.5%- Implementing monitoring, logging, and observability
  • 1. Alerting, dashboarding, and root cause analysis
  • 2. Cloud Monitoring, Logging, and Error Reporting
- 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
Topic 6: Analyzing and optimizing technical and business processes15%- Optimizing cost and resource efficiency
  • 1. Resource right-sizing and usage monitoring
  • 2. Committed use discounts and pricing models
- Streamlining operational workflows
  • 1. Change management and incident response
  • 2. Stakeholder communication and alignment
- Improving reliability and performance
  • 1. Latency reduction and throughput optimization
  • 2. SLO, SLA, and error budgeting

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Google Certified Professional - Cloud Architect (GCP) 認定 Professional-Cloud-Architect 試験問題 (Q82-Q87):

質問 # 82
You want to enable your running Google Container Engine cluster to scale as demand for your application changes.
What should you do?

正解：A

解説：
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler
Cluster autoscaling
--enable-autoscaling
Enables autoscaling for a node pool.
Enables autoscaling in the node pool specified by --node-pool or the default node pool if --node-pool is not provided.
Where:
--max-nodes=MAX_NODES
Maximum number of nodes in the node pool.
Maximum number of nodes to which the node pool specified by --node-pool (or default node pool if unspecified) can scale.
Incorrect Answers:
C, D: Warning: Do not use Alpha Clusters or alpha features for production workloads.
Note: You can experiment with Kubernetes alpha features by creating an alpha cluster. Alpha clusters are short-lived clusters that run stable Kubernetes releases with all Kubernetes APIs and features enabled. Alpha clusters are designed for advanced users and early adopters to experiment with workloads that take advantage of new features before those features are production-ready. You can use Alpha clusters just like normal Kubernetes Engine clusters.


質問 # 83
You have an application deployed on Kubernetes Engine using a Deployment named echo-deployment. The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?

正解：B

解説：
https://cloud.google.com/kubernetes-engine/docs/how-to/updating-apps#updating_an_application


質問 # 84
You are tasked with building an online analytical processing (OLAP) marketing analytics and reporting tool.
This requires a relational database that can operate on hundreds of terabytes of data. What is the Google-
recommended tool for such applications?

正解：B

解説：
Explanation/Reference:
Reference: https://cloud.google.com/files/BigQueryTechnicalWP.pdf


質問 # 85
A development team at your company has created a dockerized HTTPS web application. You need to deploy the application on Google Kubernetes Engine (GKE) and make sure that the application scales automatically.
How should you deploy to GKE?

正解：A

解説：
Explanation/Reference: https://cloud.google.com/kubernetes-engine/docs/how-to/cluster-autoscaler


質問 # 86
For this question, refer to the JencoMart case study.
JencoMart wants to move their User Profiles database to Google Cloud Platform. Which Google Database should they use?

正解：C

解説：
https://cloud.google.com/datastore/docs/concepts/overview


質問 # 87
......

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