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Google PCA Practice Question: Analysing and Optimising Technical and Business Processes

A team uses BigQuery for analytics. They notice that queries against a table with billions of rows are slow and expensive. The table is partitioned by ingestion time and has no clustering. Queries frequently filter on a 'customer_id' column. Which optimization would MOST reduce query cost and latency?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Cluster the table on customer_id

Clustering on 'customer_id' will physically co-locate rows with the same customer_id within each partition. This allows BigQuery to prune blocks, reducing the amount of data scanned for queries that filter on customer_id. Partitioning alone is not enough; clustering on the filter column is key.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use a materialized view pre-aggregated by customer_id

    Why it's wrong here

    A materialized view would help if the queries are aggregating by customer_id, but the stem says queries filter on customer_id (e.g., WHERE customer_id = X). Clustering is more direct for filtering.

  • Switch to on-demand pricing

    Why it's wrong here

    On-demand pricing charges per byte scanned; clustering reduces bytes scanned, which lowers cost regardless of pricing model.

  • Add a clustered index on customer_id

    Why it's wrong here

    BigQuery does not support indexes; it uses clustering to physically order data.

  • Cluster the table on customer_id

    Why this is correct

    Clustering on customer_id will group rows with the same customer_id together, reducing data scanned when filtering on that column.

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