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Google PCA Practice Question: Analyze and optimize technical and business processes

A company is using BigQuery for analytics and wants to optimize query costs. They have many ad-hoc queries that scan large tables. What is the best practice?

⚠ Common exam trap

Google Cloud often tests the misconception that flat-rate pricing or BI Engine directly reduce per-query costs, when in fact they address capacity or latency, not the fundamental cost driver of bytes scanned.

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

Use clustering and partitioning on tables.

Clustering and partitioning reduce the amount of data scanned by BigQuery for each query, directly lowering query costs (which are based on bytes processed). Partitioning allows queries to skip entire partitions based on a date or timestamp column, while clustering sorts data within partitions, enabling block-level pruning for filter predicates. This is the most effective and scalable way to optimize ad-hoc queries on large tables without changing the query logic.

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 clustering and partitioning on tables.

    Why this is correct

    Clustering and partitioning organize data to minimize scanned bytes, lowering per-query cost.

  • Use flat-rate pricing.

    Why it's wrong here

    Flat-rate pricing offers fixed cost but does not reduce the amount of data scanned; may be more expensive for low usage.

  • Use BI Engine.

    Why it's wrong here

    BI Engine accelerates interactive dashboards, not ad-hoc SQL queries.

  • Use materialized views.

    Why it's wrong here

    Materialized views can reduce query cost by precomputing results, but they incur storage and maintenance costs.

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Written by Johnson Ajibi, MSc IT Security

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