PROFESSIONAL-CLOUD-ARCHITECT Question #312: 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. Helicopter Racing League (HRL) wants to create and update predictions on the results of the championships, with data that collects during the rages. HRL wants to create long-term Forecasts with the data from video collected both while taking (first processing) and during streaming for users. HLR want to exploit also existing video content that is stored in object storage with their existing cloud provider. On the advice of the Cloud Architects, they decided to use the following strategies: 1. Creating experimental forecast models with minimal code in the powerful GCP environment, using also the data already collected 2. The ability and culture to develop highly customized models that are continuously improved with the data that it gradually collects. They plan to try multiple open source frameworks 3. To Integrate teamwork and create/optimize MLOps processes 4. To Serve the models in an optimized environment Which of the following GCP services do you think are the best given these requirements?

Options

• AVideo Intelligence
• BTensorFlow Enterprise and KubeFlow for the customized models
• CBigQuery ML
• DVertex-AI
• EKubernetes and TensorFlow Extend

Explanation

This question tests knowledge of selecting the most appropriate managed AI/ML platform on GCP for training, deploying, and serving customized machine learning models for race predictions.

Common mistakes.

• A. Video Intelligence API analyzes video content for labels, objects, and events using pre-trained models; it is not designed for training and serving custom telemetry-based race prediction models.
• B. TensorFlow Enterprise and KubeFlow require managing Kubernetes clusters and ML infrastructure manually, contradicting HRL's requirement for managed AI services and increasing operational overhead.
• C. BigQuery ML trains SQL-based models directly on tabular data in BigQuery but is limited in model architecture complexity and is not the best fit for real-time low-latency prediction serving required during live races.
• E. Running Kubernetes with TensorFlow Extended requires building, managing, and scaling your own ML infrastructure, which is the opposite of the managed AI services HRL wants to adopt.

Concept tested. Vertex AI managed ML platform for custom model training and serving

Reference. https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform

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