ExpertGCP PCA · Question 16 · Domain 1: Designing and Planning a Cloud Solution Architecture
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars.
Current Technical Environment:
- 1 million connected cars currently on the road
- Cars send telemetry data (speed, engine temp, location) every 5 seconds
- Current on-premises MQTT brokers are crashing under the load
Business Requirements:
- Enable predictive maintenance to alert drivers before parts fail
- Provide real-time fleet tracking for commercial customers
- Support over-the-air (OTA) software updates
Executive Statements:
- CEO: "Data is our new revenue stream. We need to monetize this telemetry data."
- CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention."
- CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity."
Technical Requirements:
- Ingest up to 100,000 messages per second
- Low-latency processing for real-time alerts
- Time-series data storage for historical analysis
- Handle variable network connectivity (cars driving through tunnels)
Constraints:
- Strict budget for data ingestion
- Small data engineering team
QUESTION:
To meet the CTO's requirement for infinite scaling and the technical requirement to ingest 100,000 messages per second, which ingestion and processing pipeline should you design?
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars.
Current Technical Environment:
- 1 million connected cars currently on the road
- Cars send telemetry data (speed, engine temp, location) every 5 seconds
- Current on-premises MQTT brokers are crashing under the load
Business Requirements:
- Enable predictive maintenance to alert drivers before parts fail
- Provide real-time fleet tracking for commercial customers
- Support over-the-air (OTA) software updates
Executive Statements:
- CEO: "Data is our new revenue stream. We need to monetize this telemetry data."
- CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention."
- CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity."
Technical Requirements:
- Ingest up to 100,000 messages per second
- Low-latency processing for real-time alerts
- Time-series data storage for historical analysis
- Handle variable network connectivity (cars driving through tunnels)
Constraints:
- Strict budget for data ingestion
- Small data engineering team
QUESTION:
To meet the CTO's requirement for infinite scaling and the technical requirement to ingest 100,000 messages per second, which ingestion and processing pipeline should you design?
Answer options:
Ingest messages into Cloud SQL, process them with Compute Engine cron jobs, and store the results in BigQuery.
Ingest messages into Cloud Pub/Sub, process them with Cloud Dataflow, and store the results in Cloud Bigtable.
Ingest messages using an HTTP Load Balancer to Cloud Run, and store directly in Cloud Storage.
Use Apache Kafka on Compute Engine for ingestion, Apache Spark on Dataproc for processing, and Cassandra on Compute Engine for storage.
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