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Google Professional-Cloud-Architect Exam Syllabus Topics:

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

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q113-Q118):

NEW QUESTION # 113
Case Study: 9 - 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.
For this question, refer to the Helicopter Racing League (HRL) case study. HRL wants better prediction accuracy from their ML prediction models. They want you to use Google's AI Platform so HRL can understand and interpret the predictions. What should you do?

Answer: D

Explanation:
AI Explanations helps you understand your model's outputs for classification and regression tasks. Whenever you request a prediction on AI Platform, AI Explanations tells you how much each feature in the data contributed to the predicted result. You can then use this information to verify that the model is behaving as expected, recognize bias in your models, and get ideas for ways to improve your model and your training data.
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview


NEW QUESTION # 114
You are designing an application for use only during business hours. For the minimum viable product release, you'd like to use a managed product that automatically "scales to zero" so you don't incur costs when there is no activity.
Which primary compute resource should you choose?

Answer: A


NEW QUESTION # 115
Your team is developing a web application that will be deployed on Google Kubernetes Engine (GKE). Your CTO expects a successful launch and you need to ensure your application can handle the expected load of tens of thousands of users. You want to test the current deployment to ensure the latency of your application stays below a certain threshold. What should you do?

Answer: D

Explanation:
The question focuses more on the current infra and ensuring if the current setup will ensure a latency target. An only a load test can do that. Autoscaling is no need of the hour and may require in the future and that totally depends on the test results. It might be an overkill to have everything in advance even App is fine with current configs.


NEW QUESTION # 116
You have deployed an application to Google Kubernetes Engine (GKE), and are using the Cloud SQL proxy container to make the Cloud SQL database available to the services running on Kubernetes. You are notified that the application is reporting database connection issues. Your company policies require a post- mortem. What should you do?

Answer: C


NEW QUESTION # 117
You are migrating your on-premises solution to Google Cloud in several phases. You will use Cloud VPN to maintain a connection between your on-premises systems and Google Cloud until the migration is completed.
You want to make sure all your on-premises systems remain reachable during this period. How should you organize your networking in Google Cloud?

Answer: A


NEW QUESTION # 118
......

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