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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. Resource right-sizing and usage monitoring
  • 2. Committed use discounts and pricing models
- Streamlining operational workflows
  • 1. Stakeholder communication and alignment
  • 2. Change management and incident response
- Improving reliability and performance
  • 1. SLO, SLA, and error budgeting
  • 2. Latency reduction and throughput optimization
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. Load balancing and CDN configuration
  • 2. VPC design, peering, and hybrid connectivity
- Provisioning and managing compute resources
  • 1. VMs, GKE, App Engine, Cloud Run deployment and management
  • 2. Auto-scaling and resource management
Designing for security and compliance17.5%- 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
- Enforcing compliance and governance
  • 1. VPC Service Controls and security perimeters
  • 2. Security controls, auditing, and logging
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
Managing implementation12.5%- Guiding development and deployment teams
  • 1. CI/CD pipelines and automation
  • 2. Application and infrastructure deployment strategies
- Using Google Cloud tools and APIs programmatically
  • 1. Cloud SDK, Cloud Shell, and Cloud Code
  • 2. Infrastructure as Code (Terraform, Deployment Manager)
- Testing and validating solutions
  • 1. Functional, performance, and security testing
  • 2. Disaster recovery and failover testing
Designing and planning a cloud solution architecture25%- Planning cloud migration and hybrid/multi-cloud environments
  • 1. Data transfer and synchronization
  • 2. Interoperability and integration design
  • 3. Migration planning and execution
- 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
- Evaluating and selecting Google Cloud services
  • 1. Compute, storage, database, and networking service selection
  • 2. Serverless and managed service adoption

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

NEW QUESTION # 28
For this question refer to the TerramEarth case study.
Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption.

Answer: C

Explanation:
Topic 1, TerramEarth Case Study
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.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
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-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, 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, without increasing the cost of carrying surplus inventory
* Support the dealer network with more data on how their customers use their equipment IP 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 CEO Statement We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment.
With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower 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. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.


NEW QUESTION # 29
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

Answer: C


NEW QUESTION # 30
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

Answer: D

Explanation:
BigQuery is Google's serverless, highly scalable, low cost enterprise data warehouse designed to make all your data analysts productive. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL and you don't need a database administrator.
BigQuery enables you to analyze all your data by creating a logical data warehouse over managed, columnar storage as well as data from object storage, and spreadsheets.
References: https://cloud.google.com/bigquery/
Reference:
Google Big Query is for multi peta byte storage , HA(High availability) which means 24 hours, SQL interface .
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/


NEW QUESTION # 31
Your company is forecasting a sharp increase in the number and size of Apache Spark and Hadoop jobs being run on your local datacenter. You want to utilize the cloud to help you scale this upcoming demand with the least amount of operations work and code change.
Which product should you use?

Answer: A

Explanation:
Google Cloud Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Cloud Dataproc provisions big or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud Platform services, such as Google Cloud Storage and Stackdriver Logging, thus helping you reduce TCO.
Reference: https://cloud.google.com/dataproc/docs/resources/faq


NEW QUESTION # 32
You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address. You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly. What should you do?

Answer: B

Explanation:
Explanation: https://cloud.google.com/vpc/docs/using-firewalls


NEW QUESTION # 33
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