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 sentimentIncrease 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 -
Click on the arrows to vote for the correct answer
A. B. C. D.A.
https://cloud.google.com/load-balancing/docs/httpsNone of the given options seems to be a correct answer for the provided scenario.
Option A seems to be updating a security policy rule for the Fastly CDN (Content Delivery Network). However, there is no indication in the scenario that HRL is using Fastly or needs to update its security policy rule.
Option B seems to be updating a firewall rule for a specific source IP list. While this may be relevant in some scenarios, it is not clear how this would help HRL migrate their existing service to a new platform, expand their use of managed AI and ML services, and move their content closer to their users.
Option C seems to be updating a firewall rule with a target tag for a specific source IP list. Again, while this may be relevant in some scenarios, it is not clear how this would help HRL achieve their business and technical requirements.
Option D seems to be updating a security policy rule for a specific policy by evaluating a pre-configured expression for a source IP list. While this may be relevant in some scenarios, it is not clear how this would help HRL achieve their business and technical requirements.
Therefore, it is recommended to review the question or provide additional context to determine the correct answer.