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NEW QUESTION 20
Your development teams release new versions of games running on Google Kubernetes Engine (GKE) daily.
You want to create service level indicators (SLIs) to evaluate the quality of the new versions from the user’s perspective. What should you do?

  • A. Create Server Uptime and Error Rate as service level indicators.
  • B. Create GKE CPU Utilization and Memory Utilization as service level indicators.
  • C. Create CPU Utilization and Request Latency as service level indicators.
  • D. Create Request Latency and Error Rate as service level indicators.

Answer: C

Explanation:
Topic 8, Helicopter Racing League Case
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.
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.
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.
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:
Race results
Mechanical failures
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.
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.
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.

 

NEW QUESTION 21
For this question, refer to the JencoMart case study.
JencoMart has built a version of their application on Google Cloud Platform that serves traffic to Asia. You want to measure success against their business and technical goals.
Which metrics should you track?

  • A. Total visits and average latency for users in Asia
  • B. Error rates for requests from Asia
  • C. Total visits, error rates, and latency from Asia
  • D. The number of character sets present in the database
  • E. Latency difference between US and Asia

Answer: A

Explanation:
Topic 4, Dress4Win case studyCompany 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.
Company Background
Dress4win’s application has grown from a few servers in the founder’s garage to several hundred servers and appliances in a colocated 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 considering moving their development and test environments. They are also considering 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.
* Databases:
* Application servers:
* Storage appliances:
* Apache Hadoop/Spark servers:
* MQ servers:
* Miscellaneous servers:
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.
* Migrate fully to the cloud if all other requirements are met.
Technical Requirements
* Evaluate and choose an automation framework for provisioning resources in cloud.
* Support failover of the production environment to cloud during an emergency.
* Identify production services that can migrate to cloud to save capacity.
* Use managed services whenever possible.
* Encrypt data on the wire and at rest.
* Support multiple VPN connections between the production data center and cloud environment.
CEO Statement
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement
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 puts a cloud strategy between 30 to 50% lower than our current model.

 

NEW QUESTION 22
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. Recompile the CLoakedServlet class using and MD5 hash instead of SHA1
  • B. Upload missing JAR files and redeploy your application.
  • C. Digitally sign all of your JAR files and redeploy your application

Answer: C

 

NEW QUESTION 23
……

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