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    PracticeGCP Professional Cloud ArchitectGCP Professional Cloud Architect Practice Exam 4Question 20
    Easy1 markMultiple Choice
    Domain 4: Analyzing and Optimizing Technical and Business ProcessesCloud MonitoringCloud DataflowObservability
    This question is part of a case study — click to read the full scenario(Case 16)

    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?

    View full case study page →

    GCP PCA · Question 20 · Domain 4: Analyzing and Optimizing Technical and Business Processes

    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.

    How should you monitor the health of the Dataflow streaming pipeline to ensure it is keeping up with the vehicle telemetry?

    Answer options:

    A.

    Monitor CPU utilization of the Dataflow worker nodes.

    B.

    Monitor the 'System Lag' and 'Data Watermark Age' metrics in Cloud Monitoring.

    C.

    Check the Cloud Audit Logs for error messages.

    D.

    Ping the Pub/Sub topic every 5 seconds to check for latency.

    How to approach this question

    Identify the specific metrics used to measure the health of a streaming data pipeline.

    Full Answer

    B.Monitor the 'System Lag' and 'Data Watermark Age' metrics in Cloud Monitoring.✓ Correct
    Monitor the 'System Lag' and 'Data Watermark Age' metrics in Cloud Monitoring.
    To ensure a streaming Dataflow pipeline is healthy and keeping up with ingestion, you must monitor 'System Lag' (the current maximum duration that an item has been waiting to be processed) and 'Data Watermark Age' (the age of the most recent item processed). These metrics are natively available in Cloud Monitoring.

    Common mistakes

    Relying on CPU metrics (A). A pipeline can have low CPU but still be blocked by external API calls, causing massive data lag.
    Question 19All questionsQuestion 21

    Practice the full GCP Professional Cloud Architect Practice Exam 4

    50 questions · hints · full answers · grading

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