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Manage implementation of cloud architecturehardMultiple ChoiceObjective-mapped

Google PCA Manage implementation of cloud architecture Practice Question

A company runs a real-time data analytics platform on Google Cloud that ingests streaming data from IoT devices. The architecture uses Cloud Pub/Sub to receive messages, Dataflow for processing, and BigQuery for storage. Recently, the team noticed that the processing latency has increased significantly during peak hours. Upon investigation, they found that the Dataflow pipeline is experiencing high system lag and some workers are being killed due to out-of-memory errors. The pipeline uses a fixed window of 10 seconds and writes to BigQuery using streaming inserts. The company wants to reduce latency without sacrificing data accuracy. Which course of action should they take?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Enable Dataflow streaming engine and use exactly-once processing mode

Dataflow Streaming Engine offloads the shuffle operation to a backend service, reducing memory pressure and allowing workers to handle more data. Increasing workers (A) may help but root cause is memory. Changing windowing (B) sacrifices timeliness. Dead-letter queue (C) does not address latency.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Change the windowing to a global window and use batch inserts to BigQuery

    Why it's wrong here

    Global windowing loses real-time nature; batch inserts increase latency.

  • Increase the number of Dataflow workers and machine type to handle the load

    Why it's wrong here

    May alleviate memory but at higher cost and not addressing root cause.

  • Implement a dead-letter queue for unprocessed messages and use a slower processing rate

    Why it's wrong here

    Dead-letter queue doesn't solve memory issue; slower rate increases latency.

  • Enable Dataflow streaming engine and use exactly-once processing mode

    Why this is correct

    Streaming engine reduces memory usage; exactly-once ensures accuracy.

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