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 12 · Domain 2: Managing and Provisioning a Solution Infrastructure
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 meet the 7-year cold storage requirement while adhering to the strict analytics budget, how should you configure storage?
Answer options:
Keep all 7 years of data in BigQuery active storage.
Export older BigQuery data to Cloud Storage Archive class using lifecycle policies.
Store all data in Cloud Storage Standard class.
Use Persistent Disk Snapshots for long-term retention.
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