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CASE STUDY: AutoIoT
Overview: Connected car manufacturer. 1M vehicles sending telemetry every 5 seconds.
Business: Predictive maintenance alerts, real-time fleet tracking, monetize anonymized data.
Executives:
- CEO: "Leverage AI to predict failures."
- CTO: "Current MQTT brokers crashing. Need fully managed, scalable ingestion."
- DPO: "Vehicle location is sensitive. Strip PII before analytics."
Tech: Ingest millions of msgs/sec, real-time stream processing for anomalies, store raw data for ML, sub-second queries for dashboards.
Constraints: Vehicles lose connection and send late batch data. ML models updated weekly. Strict analytics budget.
Which architecture should you design for the data ingestion and processing layer to replace the crashing MQTT brokers?
GCP PCA · Question 18 · Domain 1: Designing and Planning a Cloud Solution Architecture
CASE STUDY: AutoIoT
Overview: Connected car manufacturer. 1M vehicles sending telemetry every 5 seconds.
Business: Predictive maintenance alerts, real-time fleet tracking, monetize anonymized data.
Executives:
- CEO: "Leverage AI to predict failures."
- CTO: "Current MQTT brokers crashing. Need fully managed, scalable ingestion."
- DPO: "Vehicle location is sensitive. Strip PII before analytics."
Tech: Ingest millions of msgs/sec, real-time stream processing for anomalies, store raw data for ML, sub-second queries for dashboards.
Constraints: Vehicles lose connection and send late batch data. ML models updated weekly. Strict analytics budget.
Which database should you use to serve the real-time fleet tracking dashboard requiring sub-second queries on massive time-series data?
CASE STUDY: AutoIoT
Overview: Connected car manufacturer. 1M vehicles sending telemetry every 5 seconds.
Business: Predictive maintenance alerts, real-time fleet tracking, monetize anonymized data.
Executives:
- CEO: "Leverage AI to predict failures."
- CTO: "Current MQTT brokers crashing. Need fully managed, scalable ingestion."
- DPO: "Vehicle location is sensitive. Strip PII before analytics."
Tech: Ingest millions of msgs/sec, real-time stream processing for anomalies, store raw data for ML, sub-second queries for dashboards.
Constraints: Vehicles lose connection and send late batch data. ML models updated weekly. Strict analytics budget.
Which database should you use to serve the real-time fleet tracking dashboard requiring sub-second queries on massive time-series data?
Answer options:
Cloud SQL
Cloud Bigtable
Cloud Spanner
Cloud Storage
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