You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query…

Question

Options

• ARe-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• BRecommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• CModify your pipeline to maintain the last 3090 days of data in one table and the longer history in a different table to minimize full table scans over the entire
• DWrite an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to

• A
  71% (27)
• B
  8% (3)
• C
  18% (7)
• D
  3% (1)

Explanation

Partitioning a BigQuery table by a TIMESTAMP or DATE column (option A) solves the root cause: when a query filters on a date column, BigQuery's partition pruning eliminates irrelevant partitions from the scan, so only the relevant days are read instead of the entire table - directly reducing both query time and cost.

Why the distractors fail:

• B breaks the SQL requirement - Cloud Datalab reading raw CSV files is not a SQL workflow and adds operational friction for the Data Science team.
• C is a manual workaround that adds pipeline complexity and still doesn't prevent full scans within each table; it also doesn't scale cleanly as data grows.
• D per-day tables with wildcard queries are a legacy pattern that predates native partitioning; wildcards still risk scanning all matching tables and add naming/management overhead without the cost controls partitioning provides.

Memory tip: Think of partitioning as giving BigQuery a filing cabinet with labeled drawers - a date filter lets it open only the drawer it needs instead of dumping the entire cabinet on the floor. Anytime a GCP exam mentions "full table scans" + "cost" + "date filtering," partitioning by DATE/TIMESTAMP is almost always the answer.

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