Visual Professional-Cloud-Architect Cert Exam : Free PDF Quiz 2026 Realistic Google Visual Google Certified Professional - Cloud Architect (GCP) Cert Exam

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Google Professional-Cloud-Architect Exam Syllabus Topics:

SectionObjectives
Managing implementation- Interacting with Google Cloud programmatically
  • 1. Cloud SDK and gcloud CLI
  • 2. Infrastructure as Code (IaC)
  • 3. APIs and client libraries
- Advising development/operation team(s) to ensure successful deployment
  • 1. Application development best practices
  • 2. Testing and validation
  • 3. Deployment strategies
Designing for security and compliance- Designing for security
  • 1. Application security
  • 2. Data security and encryption
  • 3. Identity and Access Management (IAM)
  • 4. Network security
- Designing for compliance
  • 1. Data residency and sovereignty
  • 2. Regulatory and compliance requirements
  • 3. Audit logging and monitoring
Managing and provisioning a solution infrastructure- Configuring network topologies
  • 1. VPC design and peering
  • 2. Private access and VPN configuration
  • 3. Hybrid and multi-cloud connectivity
- Configuring individual storage systems
  • 1. Cloud Storage configuration
  • 2. Cloud SQL and Spanner
  • 3. Datastore and Bigtable
- Configuring compute systems
  • 1. Cloud Run and Cloud Functions
  • 2. GKE configuration and management
  • 3. Compute Engine configuration
Ensuring solution and operations reliability- Monitoring/logging/profiling/alerting solution
  • 1. Error reporting and alerting
  • 2. Performance profiling and tracing
  • 3. Cloud Monitoring and Cloud Logging
- Deployment and release management
  • 1. Deployment strategies and rollback
  • 2. Release management and versioning
- Evaluating quality control measures
- Assisting with support
  • 1. Incident response and troubleshooting
  • 2. Capacity planning and scaling
Analyzing and optimizing technical and business processes- Analyzing and defining technical processes
  • 1. Software development lifecycle (SDLC)
  • 2. CI/CD pipeline design
  • 3. Incident management and troubleshooting
- Analyzing and defining business processes
  • 1. Cost management and optimization
  • 2. Business KPIs and metrics
  • 3. Stakeholder communication
Designing and planning a cloud solution architecture- Designing network, storage, and compute resources
  • 1. Network design including load balancing and firewall rules
  • 2. GKE cluster and workload design
  • 3. Compute engine selection and configuration
  • 4. Storage options and data lifecycle management
- Designing solution infrastructure that meets business requirements
  • 1. Data and storage architecture
  • 2. Cost optimization
  • 3. Networking and compute resources
  • 4. Migration planning
  • 5. Business use cases and product strategy
  • 6. Integration with existing systems
- Enabling future cloud technology
  • 1. Designing for scalability and elasticity
  • 2. Implementing future-proof architectures
- Creating a migration plan
  • 1. Refactoring and rearchitecting applications
  • 2. Identifying workloads for migration
  • 3. Planning data migration

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q100-Q105):

NEW QUESTION # 100
One of your primary business objectives is being able to trust the data stored in your application. You want to log all changes to the application data. How can you design your logging system to verify authenticity of your logs?

Answer: B


NEW QUESTION # 101
You are running a cluster on Kubernetes Engine to serve a web application. Users are reporting that a specific part of the application is not responding anymore. You notice that all pods of your deployment keep restarting after 2 seconds. The application writes logs to standard output. You want to inspect the logs to find the cause of the issue. Which approach can you take?

Answer: A

Explanation:
For container and system logs, GKE deploys a per-node logging agent that reads container logs, adds helpful metadata, and then stores them. The logging agent checks for container logs in the following sources:Standard output and standard error logs from containerized processes.
https://cloud.google.com/monitoring/kubernetes-engine/legacy-stackdriver/logging


NEW QUESTION # 102
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?

Answer: A

Explanation:
Topic 7, TerramEarth
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment
TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
Decrease unplanned vehicle downtime to less than 1 week.
Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast-growing agricultural business - to create compelling joint offerings for their customers.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.
Create a backup strategy.
Increase security of data transfer from equipment to the datacenter.
Improve data in the data warehouse.
Use customer and equipment data to anticipate customer needs.
Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server
- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.


NEW QUESTION # 103
Your product learn is building a critical, customer-facing application on Google Cloud The development team wants to use Spanner for their database to take advantage of its horizontal scalability and low operational overhead However, the FinOps team is concerned about the direct monthly cost of Spanner and proposed using a self-managed PostgreSQL database on Compute Engine VMs instead. You need to resolve this conflict and ensure the project moves forward with an architecturally sound database choice that balances technical requirements with financial constraints. What should you do?

Answer: B

Explanation:
This question highlights the classic conflict between Sticker Price (direct cost) and Operational Expense (OpEx). While a Compute Engine VM running PostgreSQL (Option C) looks cheaper on a monthly bill, it ignores the cost of human labor. A Total Cost of Ownership (TCO) analysis (Option B) is the professional architectural approach to resolve this. It accounts for the salary of DBAs required to manage sharding, patching, and manual failovers-tasks that Spanner handles automatically. For a " critical, customer-facing application " requiring horizontal scale, the TCO will almost always show that Spanner is cheaper in the long run because it reduces the " 900-hour time sink " of manual database maintenance.


NEW QUESTION # 104
You are developing your microservices application on Google Kubernetes Engine. During testing, you want to validate the behavior of your application in case a specific microservice should suddenly crash. What should you do?

Answer: A

Explanation:
Microservice runs on all nodes. The Micro service runs on Pod, Pod runs on Nodes. Nodes is nothing but Virtual machines. Once deployed the application microservices will get deployed across all Nodes.
Destroying one node may not mimic the behaviour of microservice crashing as it may be running in other nodes.
link: https://istio.io/latest/docs/tasks/traffic-management/fault-injection/


NEW QUESTION # 105
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