Google-Professional-Cloud-Architect-Practice-Test-and-HTML-Quiz-Example/moreinfo.html at main · TheR20/Google-Professional-Cloud-Architect-Practice-Test-and-HTML-Quiz-Example · GitHub

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    <title>Google Professional Cloud Architect Practice Exam</title>

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<h1>Questions with *</h1>
    <h2>Introductory Info</h2>
    <h4>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.</h4>
     <h4>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.</h4>
      <h4>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.</h4>
       <h4>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.</h4>
        <h4>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
- No redundancy
- Backed up every 12 hours
- 100% uptime service level agreement (SLA)
- Authenticates all users</h4>
<h4>Compute -
30 machines in US West Coast, each machine has:
- Twin, dual core CPUs
- 32 GB of RAM
- Twin 250 GB HDD (RAID 1)
20 machines in US East Coast, each machine has:
- Single, dual-core CPU
- 24 GB of RAM
- Twin 250 GB HDD (RAID 1)
</h4>
<h4>Storage -
Access to shared 100 TB SAN in each location
Tape backup every week</h4>
<h4>Business Requirements -
Optimize for capacity during peak periods and value during off-peak periods
Guarantee service availability 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</h4>
<h4>Technical Requirements -
Assess key application for cloud suitability
Modify applications 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
</h4>
<h4>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 policies.

CTO Statement -
The challenges of operating data centers prevent 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 to 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 periods and reduce costs.</h4>

<h1>Questions with **</h1>
<h2>Introductory Info</h2>
<h4>Company overview -
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.</h4>
<h4>Solution concept -
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.</h4>
<h4>Existing technical environment -
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
Existing content is stored in an object storage service on their existing public cloud provider.
Video encoding and transcoding is performed on VMs created for each job.
Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.</h4>
<h4>Business requirements -
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
Support ability to expose the predictive models to partners.
Increase predictive capabilities during and before races:
ג—‹ Race results
ג—‹ Mechanical failures
ג—‹ Crowd sentiment
Increase telemetry and create additional insights.
Measure fan engagement with new predictions.
Enhance global availability and quality of the broadcasts.
Increase the number of concurrent viewers.
Minimize operational complexity.
Ensure compliance with regulations.
Create a merchandising revenue stream.</h4>
<h4>
Technical requirements -
Maintain or increase prediction throughput and accuracy.
Reduce viewer latency.
Increase transcoding performance.
Create real-time analytics of viewer consumption patterns and engagement.
Create a data mart to enable processing of large volumes of race data.</h4>
<h4>Executive statement -
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.</h4>

<h1>Questions with ***</h1>
    <h2>Introductory Info</h2>
    <h4></h4>
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