Question 1

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?

Accepted Answer

Look for the fully managed GCP services for streaming ingestion (Pub/Sub) and real-time processing (Dataflow).

Question 2

What mistakes do candidates make on this GCP PCA question?

Accepted Answer

Choosing direct to BigQuery (D). While BigQuery can ingest streaming data, you lose the ability to perform real-time anomaly detection and PII redaction in-flight.

How to approach this question

Full Answer

Common mistakes

Practice the full GCP Professional Cloud Architect Practice Exam 4

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