Question 1

CASE STUDY: ManuIoT

Overview:
Industry: Manufacturing
Size: 100 factories globally

Environment:
- 100,000 sensors
- Local SCADA
- Fragmented SQL Server DBs
- No central analytics

Requirements:
- Predictive maintenance
- Real-time global dashboards
- Edge computing

Exec Statements:
- CEO: Monetize telemetry.
- CFO: Costs must scale linearly.
- VP Ops: Factory lines need local control if internet drops.

Tech Reqs:
- Ingest 1M msgs/sec
- Stream processing
- Offline factory capabilities
- Train ML centrally, deploy to edge

Constraints:
- Low bandwidth/high latency at factories
- Legacy MQTT protocol
- Zero IT staff at factories

QUESTION: To meet the requirement of training ML models centrally and deploying them to the edge, which GCP AI service should you utilize?

Accepted Answer

Identify GCP's unified machine learning platform.

Question 2

What mistakes do candidates make on this GCP PCA question?

Accepted Answer

Selecting pre-trained APIs (B, C) instead of a custom model training platform.

How to approach this question

Full Answer