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

A company wants to reduce costs for their BigQuery workloads. Which TWO actions will help achieve this?

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 clustered and partitioned tables to limit data scanned

Using flat-rate pricing with slot reservations provides predictable costs, and using clustered and partitioned tables reduces the amount of data scanned per query, lowering on-demand costs. Materialized views can reduce costs but are not always needed. Deleting datasets and compression are not relevant.

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 clustered and partitioned tables to limit data scanned

    Why this is correct

    Clustering and partitioning reduce the data processed, lowering costs.

  • Use flat-rate pricing with BigQuery slot reservations

    Why this is correct

    Flat-rate pricing provides cost predictability and can be cheaper for high-usage workloads.

  • Enable compression on exported data

    Why it's wrong here

    Compression affects export costs, not query costs.

  • Delete unnecessary datasets

    Why it's wrong here

    Deleting datasets reduces storage costs but is not a primary cost optimization for queries.

  • Use materialized views to pre-compute results

    Why it's wrong here

    Materialized views can reduce query costs but may incur storage costs and are not always the best cost reduction method.

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