Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
A team runs periodic BigQuery queries on a large dataset. They notice high costs due to full table scans. They want to reduce costs and improve query performance. Which two actions should they take? (Choose two options that best fit the scenario.)
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 a date/timestamp column
The correct actions are B (partition the table by a date/timestamp column) and D (cluster the table on frequently filtered columns). Partitioning restricts scans to only the relevant date partitions, and clustering physically sorts data by the filtered columns so BigQuery prunes blocks it doesn't need, both directly cutting bytes scanned and cost. Option A (avoiding SELECT *) is a general best practice but doesn't address full table scans on a large dataset, and option C (materialized views) helps only for recurring aggregate patterns, not the broad scan-cost problem described.
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 SELECT * only when necessary
Why it's wrong here
Avoiding SELECT * reduces bytes scanned only when columns are genuinely unused; it does not address the full scans themselves. It is tempting as a cheap habit, and would be correct for wide tables where queries need few columns.
- ✓
Partition the table by a date/timestamp column
Why this is correct
Partitioning splits the table by date or timestamp, so a query with a date filter prunes irrelevant partitions and scans only the matching ones. This reduces bytes processed, lowering cost and improving performance versus full table scans.
- ✗
Use materialized views to pre-aggregate data
Why it's wrong here
Materialised views pre-aggregate results but do not eliminate full scans on the underlying base tables for ad-hoc queries. It is tempting because they cut cost for repeated aggregations, and would be correct for recurring dashboard queries with stable patterns.
- ✓
Cluster the table on frequently filtered columns
Why this is correct
Clustering sorts data within each partition by the clustered columns, so BigQuery reads only the blocks matching the filter rather than scanning the whole table. This directly cuts bytes processed and cost for queries filtering on those columns.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Column
A column is a vertical set of values in a database table that stores one specific type of attribute for every row.
Key term
Table
A table is a structured collection of data organized into rows and columns, used in databases and spreadsheets to store and manage information efficiently.
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