Crucial Exams

GCP Professional Data Engineer Practice Question

Your company streams purchase events from Pub/Sub into BigQuery for near real-time dashboards. Compliance requires that any primary account number (PAN) in the field card_number is tokenized before it is written. Business analysts also need each record to contain a non-empty order_id and want duplicate order_id values to be discarded if they arrive again within 24 hours. You must keep end-to-end latency below five seconds and avoid managing cluster infrastructure. Which design should you implement to satisfy the cleansing requirements?

  • Deploy a long-running Dataproc Spark Streaming job that calls Cloud DLP for tokenization, removes duplicate order_id values, stores Parquet files in Cloud Storage, and triggers a BigQuery load job every hour.

  • Use the Pub/Sub to BigQuery streaming template without modification and rely on BigQuery policy tags to mask the card_number column, accepting all rows and deduplicating later with a nightly BigQuery MERGE job.

  • Build a streaming Dataflow pipeline that invokes Cloud DLP to tokenize card_number, filters out events with a null order_id, applies a 24-hour windowed Distinct on order_id, and writes the cleansed stream to BigQuery via the Storage Write API.

  • Create an hourly Cloud Data Fusion batch pipeline that pulls messages from Pub/Sub, uses the built-in Cloud DLP plugin to tokenize card_number, deduplicates on order_id, and then loads the result into BigQuery.

GCP Professional Data Engineer
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