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CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
Which architecture should you recommend for the real-time ingestion and processing pipeline to meet the CTO's requirement for a fully managed serverless solution?
GCP PCA · Question 15 · Design for Security
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
How should you integrate the existing on-premises Active Directory with Google Cloud to manage user access for the data analysts?
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
How should you integrate the existing on-premises Active Directory with Google Cloud to manage user access for the data analysts?
Answer options:
Export users from Active Directory to a CSV file and manually import them into Cloud IAM.
Configure Active Directory as an OAuth 2.0 provider directly in BigQuery.
Use Google Cloud Directory Sync (GCDS) to synchronize users and groups from Active Directory to Cloud Identity.
Deploy a read-only domain controller (RODC) on a Compute Engine VM and point Cloud IAM to it.
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