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| Section | Weight | Objectives |
|---|
| Analyzing and optimizing technical and business processes | 15% | - Optimizing cost and resource efficiency
- 1. Resource right-sizing and usage monitoring
- 2. Committed use discounts and pricing models
- Improving reliability and performance
- 1. SLO, SLA, and error budgeting
- 2. Latency reduction and throughput optimization
- Streamlining operational workflows
- 1. Change management and incident response
- 2. Stakeholder communication and alignment
|
| Managing and provisioning solution infrastructure | 15% | - 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. Database deployment, replication, and backup
- 2. Cloud Storage, Filestore, Persistent Disk management
|
| Designing and planning a cloud solution architecture | 25% | - 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
- Planning cloud migration and hybrid/multi-cloud environments
- 1. Data transfer and synchronization
- 2. Interoperability and integration design
- 3. Migration planning and execution
- Evaluating and selecting Google Cloud services
- 1. Serverless and managed service adoption
- 2. Compute, storage, database, and networking service selection
|
| Designing for security and compliance | 17.5% | - Enforcing compliance and governance
- 1. VPC Service Controls and security perimeters
- 2. Security controls, auditing, and logging
- Implementing identity and access management
- 1. Organizational policies and resource hierarchy
- 2. IAM roles, permissions, and service accounts
- Securing data and communications
- 1. Key management and secret security
- 2. Encryption at rest and in transit
|
| Managing implementation | 12.5% | - 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
- Testing and validating solutions
- 1. Functional, performance, and security testing
- 2. Disaster recovery and failover testing
|
| Ensuring solution and operations excellence | 12.5% | - 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
- Planning for continuous improvement
- 1. Feedback loops and optimization cycles
- 2. Technology updates and architecture evolution
|
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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q84-Q89):
NEW QUESTION # 84
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 SQL
- D. Google Cloud Storage
Answer: B
Explanation:
Explanation/Reference:
Explanation:
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/
NEW QUESTION # 85
For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?
- A. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.
- B. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
- C. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.
- D. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
Answer: D
NEW QUESTION # 86
Case Study: 5 - Dress4win
Company Overview
Dress4win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster.
Dress4Win is committing to a full migration to a public cloud.
Solution Concept
For the first phase of their migration to the cloud, Dress4win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment
The Dress4win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
- MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
- Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
- 40 Web Application servers providing micro-services based APIs and static content.
- Tomcat - Java
- Nginx
- 4 core CPUs
- 32 GB of RAM
- 20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUS
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
- 3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
- Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
- iSCSI for VM hosts
- Fiber channel SAN - MySQL databases
- 1 PB total storage; 400 TB available
- NAS - image storage, logs, backups
- 100 TB total storage; 35 TB available
Business Requirements
- Build a reliable and reproducible environment with scaled parity of production.
- Improve security by defining and adhering to a set of security and Identity and Access
Management (IAM) best practices for cloud.
- Improve business agility and speed of innovation through rapid provisioning of new resources.
- Analyze and optimize architecture for performance in the cloud.
Technical Requirements
- Easily create non-production environment in the cloud.
- Implement an automation framework for provisioning resources in cloud.
- Implement a continuous deployment process for deploying applications to the on-premises
datacenter or cloud.
- Support failover of the production environment to cloud during an emergency.
- Encrypt data on the wire and at rest.
- Support multiple private connections between the production data center and cloud
environment.
Executive Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
For this question, refer to the Dress4Win case study. You want to ensure that your on-premises architecture meets business requirements before you migrate your solution.
What change in the on-premises architecture should you make?
- A. Replace RabbitMQ with Google Pub/Sub.
- B. Resize compute resources to match predefined Compute Engine machine types.
- C. Containerize the micro services and host them in Google Kubernetes Engine.
- D. Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.
Answer: C
Explanation:
Containerizing the existing applications ensures efficient use of resources. This activity the business requirement "optimize architecture for performance in the cloud". As a precursor to Cloud migration, you could convert the microservices to containers and host them on GKE on- prem: https://cloud.google.com/anthos/gke/docs/on-prem/overview which also makes it very easy for you to migrate to Cloud. GKE on-prem is hybrid cloud software that brings Google Kubernetes Engine (GKE) to on-premises data centres. With GKE on-prem, you can create, manage, and upgrade Kubernetes clusters in your on-premises environment.
NEW QUESTION # 87
You are developing a globally scaled frontend for a legacy streaming backend data API. This API expects events in strict chronological order with no repeat data for proper processing.
Which products should you deploy to ensure guaranteed-once FIFO (first-in, first-out) delivery of data?
- A. Cloud Pub/Sub to Stackdriver
- B. Cloud Pub/Sub to Cloud SQL
- C. Cloud Pub/Sub to Cloud DataFlow
- D. Cloud Pub/Sub alone
Answer: B
Explanation:
Explanation
Reference https://cloud.google.com/pubsub/docs/ordering
NEW QUESTION # 88
You deploy your custom Java application to Google App Engine. It fails to deploy and gives you the following stack trace.

What should you do?
- A. Upload missing JAR files and redeploy your application.
- B. Recompile the CLoakedServlet class using and MD5 hash instead of SHA1
- C. Digitally sign all of your JAR files and redeploy your application
Answer: C
NEW QUESTION # 89
......
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