Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A data analytics team uses BigQuery for large-scale queries. They notice that queries are scanning more data than necessary, leading to high costs. Which feature should they implement to reduce the amount of data scanned per query?
⚠ Common exam trap
Google Cloud often tests the distinction between partitioning (which reduces data scanned by pruning entire segments) and clustering (which only reorganizes data within partitions for better compression and filtering, but does not reduce the total data scanned unless combined with partitioning).
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
✓
Partitioning
Partitioning divides a table into segments based on a column (e.g., date), allowing BigQuery to prune partitions during query execution. When a query includes a filter on the partitioning column, BigQuery scans only the relevant partitions, significantly reducing the bytes processed and lowering costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Materialized views
Why it's wrong here
Materialized views cache the results of a query and can improve performance by allowing the query engine to read a smaller, precomputed result set. However, they do not inherently reduce the amount of bytes scanned from the underlying base table; if the view is not selected or is not matching the query pattern, the engine still scans the base table. The optimizer may also choose to use the base table if the view is stale or not considered beneficial, so relying on materialized views alone does not guarantee reduced scan costs.
- ✗
Streaming inserts
Why it's wrong here
Streaming inserts are a data ingestion mechanism that writes real-time events into BigQuery tables via the Storage Write API or the legacy streaming API. They directly affect how data is loaded, not how queries read it, so they do nothing to limit the volume of data processed during a query. To reduce scan size, you would still need to partition or cluster the table, and streaming inserts do not automatically enable or require those features.
- ✓
Partitioning
Why this is correct
Partitioning divides a table into separate storage segments based on a specified column, commonly a date or timestamp, and BigQuery performs partition pruning during query execution. When a query's WHERE clause filters on the partition column, the engine avoids scanning partitions that fall outside the filter, directly reducing the bytes processed and thereby lowering query cost. This is a native, deterministic way to limit scan size and is especially effective for large, time-series tables.
- ✗
Clustering
Why it's wrong here
Clustering reorganizes data within a table by sorting rows based on one or more columns, which improves the performance of filters and aggregations by enhancing block-level locality. However, clustering alone does not enable the query engine to skip entire ranges of data; without partitioning, queries still scan the whole table and incur the full byte cost. Only when clustering is combined with partitioning does it offer additional scan reduction by allowing block pruning within each selected partition.
Go deeper
Related to this question
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BigQuery and Data Analytics
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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