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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. VPC Service Controls and security perimeters
  • 2. Security controls, auditing, and logging
- Securing data and communications
  • 1. Encryption at rest and in transit
  • 2. Key management and secret security
- Implementing identity and access management
  • 1. Organizational policies and resource hierarchy
  • 2. IAM roles, permissions, and service accounts
Topic 2: Managing implementation12.5%- Testing and validating solutions
  • 1. Disaster recovery and failover testing
  • 2. Functional, performance, and security testing
- Guiding development and deployment teams
  • 1. Application and infrastructure deployment strategies
  • 2. CI/CD pipelines and automation
- Using Google Cloud tools and APIs programmatically
  • 1. Cloud SDK, Cloud Shell, and Cloud Code
  • 2. Infrastructure as Code (Terraform, Deployment Manager)
Topic 3: Designing and planning a cloud solution architecture25%- Designing cloud solution infrastructure aligned with business requirements
  • 1. Compliance and regulatory requirements consideration
  • 2. Cost optimization strategies
  • 3. Scalability, high availability, and fault tolerance design
- Evaluating and selecting Google Cloud services
  • 1. Compute, storage, database, and networking service selection
  • 2. Serverless and managed service adoption
- Planning cloud migration and hybrid/multi-cloud environments
  • 1. Data transfer and synchronization
  • 2. Migration planning and execution
  • 3. Interoperability and integration design
Topic 4: 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
- 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
Topic 5: Ensuring solution and operations excellence12.5%- Applying Well-Architected Framework principles
  • 1. Operational excellence, reliability, security, cost, and performance pillars
- 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
Topic 6: Managing and provisioning solution infrastructure15%- Provisioning and managing compute resources
  • 1. VMs, GKE, App Engine, Cloud Run deployment and management
  • 2. Auto-scaling and resource management
- Configuring network topologies and connectivity
  • 1. VPC design, peering, and hybrid connectivity
  • 2. Load balancing and CDN configuration
- Managing storage and data services
  • 1. Cloud Storage, Filestore, Persistent Disk management
  • 2. Database deployment, replication, and backup

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q28-Q33):

NEW QUESTION # 28
You need to build and deploy a containerized web application to Google Cloud. The application is very write-heavy and requires a relational database as its data store. The application needs to be highly available in multiple cloud regions. You want to minimize operational overhead while following Google-recommended practices. What should you do?

Answer: A

Explanation:
Deploying the containerized web application to Cloud Run in multiple regions behind a global HTTPS load balancer provides multi-region high availability with minimal infrastructure management. Using Spanner delivers a fully managed relational database with strong consistency and native multi-region availability, which is well suited for write-heavy workloads while following Google-recommended practices for global scalability and reliability.


NEW QUESTION # 29
You want to enable your running Google Container Engine cluster to scale as demand for your application changes.
What should you do?

Answer: D

Explanation:
Explanation
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.


NEW QUESTION # 30
You want to enable your running Google Container Engine cluster to scale as demand for your application changes. What should you do?

Answer: A

Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-autoscaler To enable autoscaling for an existing node pool, run the following command:
gcloud container clusters update [CLUSTER_NAME] --enable-autoscaling \--min-nodes 1 -- max-nodes 10 --zone [COMPUTE_ZONE] --node-pool default-pool


NEW QUESTION # 31
You are architecting a new application feature for a healthcare provider based in Qatar. The feature needs to summarize sensitive patient notes that are submitted by clinicians. A critical requirement is that the content of these patient notes must never be processed outside of Qatar ' s borders. You want to use a powerful, pre- trained generative model for the summarization task while strictly adhering to the data residency constraint.
What should you do?

Answer: D

Explanation:
Strict geographical data residency rules require data to be processed locally within a specific country ' s jurisdiction. Standard cloud-hosted, multi-tenant foundation models (such as the primary Gemini API endpoints) often routes requests across global data centers, which violates data sovereignty boundaries if a regional endpoint is unavailable.
To enforce absolute data isolation within Qatar, the architecture must utilize a local runtime model deployment. Gemma models are lightweight, open foundation models built from the same research and technology used to create the Gemini models. By selecting a pre-trained Gemma model from Model Garden and deploying it to a dedicated Vertex AI (Gemini Enterprise Agent Platform) Endpoint running inside a Google Cloud data center physically located within Qatar, the clinician text stays completely contained inside local infrastructure. This satisfies data residency requirements without needing to train a custom model from scratch.
References: Cloud Architect (GCP) Study Guide topics: " Vertex AI Model Garden and Custom Endpoints " ,
" Gemma Open Models Deployment " , " Data Sovereignty and Governance " , and " AI Infrastructure Data Residency " .


NEW QUESTION # 32
For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles.
You want to run this job on all the data. What is the most cost-effective way to run this job?

Answer: C

Explanation:
Explanation
Storageguarantees 2 replicates which are geo diverse (100 miles apart) which can get better remote latency and availability.
More importantly, is that multiregional heavily leverages Edge caching and CDNs to provide the content to the end users.
All this redundancy and caching means that Multiregional comes with overhead to sync and ensure consistency between geo-diverse areas. As such, it's much better for write-once-read-many scenarios. This means frequently accessed (e.g. "hot" objects) around the world, such as website content, streaming videos, gaming or mobile applications.
References:
https://medium.com/google-cloud/google-cloud-storage-what-bucket-class-for-the-best-performance-5c847ac8f9


NEW QUESTION # 33
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