Crucial Exams

GCP Professional Data Engineer Practice Question

You ingest 50 million new clickstream rows per day into a partitioned and clustered BigQuery table called proj.analytics.events. A Looker Studio dashboard repeatedly runs this seven-day KPI query:

SELECT DATE(event_timestamp) AS event_date, country, COUNT(*) AS sessions FROM proj.analytics.events WHERE event_timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY) GROUP BY event_date, country;

Dashboard latency now exceeds 4 seconds. You already enabled BI Engine, but the working set no longer fits in memory. Without changing any dashboard SQL, which action will most effectively cut both latency and cost?

  • Purchase a larger BI Engine reservation so the last seven days of raw data are fully cached in memory.

  • Move the table to Cloud Storage as a BigLake table and enable table-level result caching for the dashboard queries.

  • Create a materialized view that implements the same aggregation and rely on BigQuery's automatic incremental refresh and query-rewrite.

  • Replace COUNT(*) with APPROX_COUNT_DISTINCT and add a LIMIT clause to all dashboard queries.

GCP Professional Data Engineer
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