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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?
GCP PCA · Question 14 · Domain 4: Analyzing and Optimizing Technical and Business Processes
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.
To build and deploy the predictive maintenance ML models with minimal MLOps overhead, which platform should you use?
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.
To build and deploy the predictive maintenance ML models with minimal MLOps overhead, which platform should you use?
Answer options:
Compute Engine with custom TensorFlow installations.
Vertex AI
Cloud Dataproc
BigQuery ML
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