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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 20 · Domain 4: Analyzing and Optimizing Technical and Business Processes
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.
How should you monitor the health of the Dataflow streaming pipeline to ensure it is keeping up with the vehicle telemetry?
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.
How should you monitor the health of the Dataflow streaming pipeline to ensure it is keeping up with the vehicle telemetry?
Answer options:
Monitor CPU utilization of the Dataflow worker nodes.
Monitor the 'System Lag' and 'Data Watermark Age' metrics in Cloud Monitoring.
Check the Cloud Audit Logs for error messages.
Ping the Pub/Sub topic every 5 seconds to check for latency.
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