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| Section | Objectives |
|---|---|
| Topic 1: Design for security and compliance | - Implement identity and access management (IAM) - Configure data protection and encryption |
| Topic 2: Analyze and optimize technical and business processes | - Monitor, log, and optimize system performance - Optimize cost and resource usage |
| Topic 3: Manage implementations of cloud architecture | - Ensure solution reliability and operational excellence - Manage deployment and production systems |
| Topic 4: Design and plan a cloud solution architecture | - Plan security and compliance requirements - Design for reliability, scalability, and availability - Design Google Cloud solutions based on business requirements |
| Topic 5: Manage and provision the solution infrastructure | - Provision compute, storage, and network resources - Automate infrastructure deployment |
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NEW QUESTION # 155
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: D
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-5c847ac8f9f2
NEW QUESTION # 156
For this question, refer to the Dress4Win case study.
Dress4Win has asked you for advice on how to migrate their on-premises MySQL deployment to the cloud. They want to minimize downtime and performance impact to their on-premises solution during the migration. Which approach should you recommend?
Answer: A
Explanation:
Topic 5, Dress4Win Case 2
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.
NEW QUESTION # 157
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: B
Explanation:
Capacity planning, TCO calculations, opex/capex allocation From the case study, it can conclude that Management (CXO) all concern rapid provision of resources (infrastructure) for growing as well as cost management, such as Cost optimization in Infrastructure, trade up front capital expenditures (Capex) for ongoing operating expenditures (Opex), and Total cost of ownership (TCO)
Topic 4, JencoMart
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world's leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S. data centers.
Database
* Oracle Database stores user profiles
20 TB
Complex table structure
Well maintained, clean data
Strong backup strategy
* PostgreSQL database stores user credentials
Single-homed in US West
o No redundancy
o Backed up every 12 hours
100% uptime service level agreement (SLA)
Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:
o Twin, dual core CPUs
o 32GB of RAM
Twin 250 GB HDD (RAID 1)
* 20 machines in US East Coast, each machine has:
o Single dual-core CPU
o 24 GB of RAM
Twin 250 GB HDD (RAID 1)
Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through 'green' initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long-term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long-term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
NEW QUESTION # 158
Your company creates rendering software which users can download from the company website. Your
company has customers all over the world. You want to minimize latency for all your customers. You want
to follow Google-recommended practices.
How should you store the files?
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION # 159
You have been engaged by your client to lead the migration of their application infrastructure to GCP. One of their current problems is that the on-premises high performance SAN is requiring frequent and expensive upgrades to keep up with the variety of workloads that are identified as follows: 20TB of log archives retained for legal reasons; 500 GB of VM boot/data volumes and templates; 500 GB of image thumbnails; 200 GB of customer session state data that allows customers to restart sessions even if off-line for several days.
Which of the following best reflects your recommendations for a cost-effective storage allocation?
Answer: C
Explanation:
https://cloud.google.com/compute/docs/disks
NEW QUESTION # 160
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