CASE STUDY: AutoMakers Inc. 1M connected cars, 100GB/day telemetry. Req: Predictive maintenance, real-time driver dashboard, monetize data. CEO: Data is new engine. CFO: Cut 3rd-party IoT costs. CTO: Highly scalable ingest. Tech: MQTT ingest, stream processing, ML models, 7-yr cold storage, handle intermittent connectivity. Constraints: Anonymize data, low vehicle compute, strict analytics budget.
How should you architect the highly scalable ingestion layer for MQTT telemetry data from 1 million cars?
GCP PCA · Question 13 · Domain 3: Designing for Security and Compliance
CASE STUDY: AutoMakers Inc. 1M connected cars, 100GB/day telemetry. Req: Predictive maintenance, real-time driver dashboard, monetize data. CEO: Data is new engine. CFO: Cut 3rd-party IoT costs. CTO: Highly scalable ingest. Tech: MQTT ingest, stream processing, ML models, 7-yr cold storage, handle intermittent connectivity. Constraints: Anonymize data, low vehicle compute, strict analytics budget.
Which service should you integrate into the streaming pipeline to automatically anonymize Vehicle Identification Numbers (VINs) before data scientists access it?
Answer options:
Cloud Key Management Service (KMS)
Cloud Data Loss Prevention (DLP) API
Identity and Access Management (IAM)
VPC Service Controls
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