ExpertMedium1 markMultiple Choice
This question is part of a case study — click to read the full scenario(Case 16)
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: How should you architect the ingestion layer to handle 1 million MQTT messages per second from the legacy sensors?
GCP PCA · Question 19 · Technical Processes
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: Which service should you use to perform real-time anomaly detection on the streaming sensor data before it is stored?
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: Which service should you use to perform real-time anomaly detection on the streaming sensor data before it is stored?
Answer options:
A.
Cloud Dataproc
B.
Cloud Dataflow
C.
Cloud Functions
D.
BigQuery ML
How to approach this question
Identify the GCP service designed for complex, serverless stream processing.
Full Answer
B.Cloud Dataflow✓ Correct
Cloud Dataflow
Cloud Dataflow is the ideal choice for stream processing. It integrates seamlessly with Pub/Sub, scales automatically to handle 1M msgs/sec, and provides advanced windowing and stateful processing capabilities required for real-time anomaly detection.
Common mistakes
Choosing Cloud Functions (C) for stream processing. While functions can trigger on Pub/Sub, they lack the stateful windowing capabilities needed for anomaly detection.
Practice the full GCP Professional Cloud Architect Practice Exam 6
50 questions · hints · full answers · grading
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