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Domain 1: Designing and Planning a Cloud Solution ArchitectureCloud Pub/SubCloud DataflowStreaming Architecture

GCP PCA · Question 16 · Domain 1: Designing and Planning a Cloud Solution Architecture

CASE STUDY: AutoIoT

Overview: Connected car manufacturer. 1M vehicles sending telemetry every 5 seconds.
Business: Predictive maintenance alerts, real-time fleet tracking, monetize anonymized data.
Executives:

  • CEO: "Leverage AI to predict failures."
  • CTO: "Current MQTT brokers crashing. Need fully managed, scalable ingestion."
  • DPO: "Vehicle location is sensitive. Strip PII before analytics."
    Tech: Ingest millions of msgs/sec, real-time stream processing for anomalies, store raw data for ML, sub-second queries for dashboards.
    Constraints: Vehicles lose connection and send late batch data. ML models updated weekly. Strict analytics budget.

Which architecture should you design for the data ingestion and processing layer to replace the crashing MQTT brokers?

Answer options:

A.

Deploy a larger cluster of self-managed Kafka brokers on Compute Engine.

B.

Use Cloud Pub/Sub for ingestion, and Cloud Dataflow for stream processing and PII redaction.

C.

Use Cloud IoT Core for ingestion and Cloud Dataproc for batch processing.

D.

Write the telemetry data directly from the vehicles into BigQuery using the streaming API.

How to approach this question

Look for the fully managed GCP services for streaming ingestion (Pub/Sub) and real-time processing (Dataflow).

Full Answer

B.Use Cloud Pub/Sub for ingestion, and Cloud Dataflow for stream processing and PII redaction.✓ Correct
For high-throughput IoT telemetry ingestion, Cloud Pub/Sub is the standard Google Cloud fully managed service. It scales automatically to handle millions of messages per second. Cloud Dataflow (Apache Beam) is the recommended stream processing engine to read from Pub/Sub, perform real-time anomaly detection, and integrate with Cloud DLP to strip PII before writing to the final storage layer.

Common mistakes

Choosing direct to BigQuery (D). While BigQuery can ingest streaming data, you lose the ability to perform real-time anomaly detection and PII redaction in-flight.
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