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

A development team uses BigQuery for analytical queries. They want to reduce query costs for a large table that is frequently filtered by a date column and a customer_id column. Which TWO table design strategies will reduce the amount of data scanned? (Choose 2)

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

Partition the table by date.

Partitioning by date limits scans to relevant partitions. Clustering on customer_id further organizes data within partitions for efficient filtering. Both reduce data scanned. Indexes and normalization are not applicable in BigQuery.

Answer analysis

Option-by-option breakdown

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

  • Partition the table by date.

    Why this is correct

    Date partitioning allows queries to scan only relevant partitions.

  • Create an index on customer_id.

    Why it's wrong here

    BigQuery does not use traditional indexes.

  • Use wildcard tables with date suffixes.

    Why it's wrong here

    Wildcard tables are for querying multiple tables, not necessary if partitioning is used.

  • Normalize the table into multiple tables.

    Why it's wrong here

    Normalization increases joins and does not reduce scanned data.

  • Cluster the table on customer_id.

    Why this is correct

    Clustering improves filter performance by colocating data.

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