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 17 · Storage Systems
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 database service should you select to store the high-throughput time-series telemetry data for real-time dashboarding?
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 database service should you select to store the high-throughput time-series telemetry data for real-time dashboarding?
Answer options:
A.
Cloud SQL
B.
Cloud Spanner
C.
Cloud Bigtable
D.
Firestore
How to approach this question
Match the requirement 'high-throughput time-series data' to the appropriate GCP database.
Full Answer
C.Cloud Bigtable✓ Correct
Cloud Bigtable
Cloud Bigtable is Google's fully managed, scalable NoSQL database service for large analytical and operational workloads. It is optimized for time-series data and can easily handle the ingestion of 1 million messages per second with single-digit millisecond latency.
Common mistakes
Choosing Spanner (B) because it scales, without realizing Bigtable is purpose-built and more cost-effective for time-series/IoT data.
Practice the full GCP Professional Cloud Architect Practice Exam 6
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