PROFESSIONAL-CLOUD-ARCHITECT Question #195: Real Exam Question with Answer & Explanation

Question

Case Study: 9 - Helicopter Racing League 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. For this question, refer to the Helicopter Racing League (HRL) case study. A recent finance audit of cloud infrastructure noted an exceptionally high number of Compute Engine instances are allocated to do video encoding and transcoding. You suspect that these Virtual Machines are zombie machines that were not deleted after their workloads completed. You need to quickly get a list of which VM instances are idle. What should you do?

Options

• ALog into each Compute Engine instance and collect disk, CPU, memory, and network usage
• BUse the gcloud compute instances list to list the virtual machine instances that have the idle: true
• CUse the gcloud recommender command to list the idle virtual machine instances.
• DFrom the Google Console, identify which Compute Engine instances in the managed instance

Explanation

The gcloud recommender command surfaces GCP's built-in Recommender service findings, including idle VM instance recommendations, without manual inspection or scripting.

Common mistakes.

• A. Logging into each Compute Engine instance individually to collect metrics is a manual, time-consuming approach that does not scale across a large fleet and is not a GCP best practice.
• B. The gcloud compute instances list command does not have an idle: true filter flag; VM idleness is not a property exposed in the instances list output.
• D. Manually reviewing the Google Cloud Console for idle instances in a managed instance group is a non-programmatic approach that cannot be automated or audited, and does not leverage GCP's Recommender service.

Concept tested. GCP Recommender API for idle VM identification

Reference. https://cloud.google.com/recommender/docs/recommenders/compute/idle-vm

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