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CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
Which architecture should you design to handle the ingestion of 500,000 messages per second from vehicles with intermittent connectivity?
GCP PCA · Question 14 · Technical Processes
CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
Which THREE GCP services should you combine to build the pipeline for real-time anomaly detection and predictive maintenance ML training? (Select THREE)
CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
Which THREE GCP services should you combine to build the pipeline for real-time anomaly detection and predictive maintenance ML training? (Select THREE)
Answer options:
Cloud Dataflow.
Cloud SQL.
BigQuery.
Vertex AI.
Cloud Spanner.
Cloud Dataprep.
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