ExpertGCP PCA · Question 11 · Domain 1: Designing and Planning a Cloud Solution Architecture
CASE STUDY: AutoMakers Inc. 1M connected cars, 100GB/day telemetry. Req: Predictive maintenance, real-time driver dashboard, monetize data. CEO: Data is new engine. CFO: Cut 3rd-party IoT costs. CTO: Highly scalable ingest. Tech: MQTT ingest, stream processing, ML models, 7-yr cold storage, handle intermittent connectivity. Constraints: Anonymize data, low vehicle compute, strict analytics budget.
How should you architect the highly scalable ingestion layer for MQTT telemetry data from 1 million cars?
CASE STUDY: AutoMakers Inc. 1M connected cars, 100GB/day telemetry. Req: Predictive maintenance, real-time driver dashboard, monetize data. CEO: Data is new engine. CFO: Cut 3rd-party IoT costs. CTO: Highly scalable ingest. Tech: MQTT ingest, stream processing, ML models, 7-yr cold storage, handle intermittent connectivity. Constraints: Anonymize data, low vehicle compute, strict analytics budget.
How should you architect the highly scalable ingestion layer for MQTT telemetry data from 1 million cars?
Answer options:
Use Cloud IoT Core directly.
Deploy an MQTT broker on GKE and publish messages to Cloud Pub/Sub.
Have cars write directly to BigQuery via streaming inserts.
Use Cloud Functions to receive HTTP POST requests from cars.
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