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GCP Associate Cloud Engineer Practice Question

Your team ingests clickstream events into a Pub/Sub topic. Traffic is highly bursty (up to 500 000 events per second). Each record must be parsed, enriched with a lookup table stored in Cloud Storage, placed into one-minute tumbling windows, and then written to BigQuery so near-real-time dashboards stay current. The same transformation logic must also be reused every weekend to reprocess a month of raw data that is stored in Cloud Storage, without modifying code or provisioning extra infrastructure. Which Google Cloud service should you build on to satisfy these requirements while minimizing operational overhead?

  • BigQuery scheduled queries that read raw files from Cloud Storage and update reporting tables

  • Dataflow with an Apache Beam pipeline that reads from Pub/Sub or Cloud Storage and writes to BigQuery

  • Cloud Functions triggered by Pub/Sub that stream each message directly into BigQuery

  • A Dataproc cluster running Spark Streaming jobs for live data and separate Spark batch jobs for backfills

GCP Associate Cloud Engineer
Planning and implementing a cloud solution
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