Crucial Exams

GCP Professional Data Engineer Practice Question

A streaming analytics workload writes millions of rows per hour into the partitioned table clickstream.events (partitioned by the event_date column). To accelerate dashboard queries that aggregate the number of page views per day, you create the following materialized view:

CREATE MATERIALIZED VIEW marketing.mv_daily_views
PARTITION BY event_date AS
SELECT
  event_date,
  COUNT(*) AS views
FROM `marketing.clickstream.events`
GROUP BY event_date;

After deployment, you notice that the view's query latency is consistently low even though the base table continues to grow rapidly. What explains this behavior?

  • A manual Cloud Scheduler job is automatically created during CREATE MATERIALIZED VIEW to refresh the view after every load job.

  • BigQuery rebuilds the entire materialized view on a 30-minute interval, which masks the growing size of the base table during rebuilds.

  • The materialized view is refreshed only when it is queried; the low latency occurs because no one has queried it since creation.

  • BigQuery updates only the partitions of the materialized view that correspond to newly inserted or modified event_date values, so most of the cached results remain valid and can be served immediately.

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