Question 1

**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:**
The operations team needs to be alerted if the number of unacknowledged messages in the Pub/Sub subscription exceeds 500,000, indicating a processing bottleneck. How should you configure this?

Accepted Answer

Identify the need: alert on a specific GCP resource metric (Pub/Sub backlog). Cloud Monitoring is the native tool for metric-based alerting.

Question 2

What mistakes do candidates make on this GCP PCA question?

Accepted Answer

Choosing dead-letter topics (D). Dead-letter topics handle poison pill messages (bad data), not processing latency or backlogs.

How to approach this question

Full Answer

Common mistakes

Practice the full GCP Professional Cloud Architect Practice Exam 3

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