🔥 40% Off Crucial Exams Memberships — Deal ends today!

1 hour, 51 minutes remaining!
Crucial Exams

GCP Professional Data Engineer Practice Question

Your retail analytics team ingests 5 TB of point-of-sale records into a single BigQuery table every day. Analysts typically run interactive SQL that filters on sale_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY) AND CURRENT_DATE() and aggregates by store_id and product_category. Queries are becoming slower and more expensive, but the upstream ingestion pipeline cannot be modified. Which BigQuery design change will most effectively reduce both query latency and bytes scanned costs?

  • Create a materialized view that pre-aggregates sales for the last 90 days and refresh it on demand.

  • Normalize the schema by moving store and product attributes into separate dimension tables and leaving only foreign keys in the fact table.

  • Convert the table to a partitioned table on sale_date and add clustering columns for store_id and product_category.

  • Replace the single table with daily sharded tables named sales_YYYYMMDD and query them with table wildcards.

GCP Professional Data Engineer
Storing the data
Your Score:
Settings & Objectives
Random Mixed
Questions are selected randomly from all chosen topics, with a preference for those you haven’t seen before. You may see several questions from the same objective or domain in a row.
Rotate by Objective
Questions cycle through each objective or domain in turn, helping you avoid long streaks of questions from the same area. You may see some repeat questions, but the distribution will be more balanced across topics.

Check or uncheck an objective to set which questions you will receive.

Wipe Score
Bash, the Crucial Exams Chat Bot
AI Bot