Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
A data analytics team runs ad-hoc SQL queries on BigQuery to explore a 10 TB table. Queries are slow and expensive because they frequently scan the entire table. They want to reduce query costs and improve performance without changing the table schema. Which optimization should they apply first?
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 or timestamp column.
Partitioning the table by a time column (e.g., ingestion date) allows queries to filter on that column and scan only relevant partitions, reducing scanned data and cost. Clustering is also beneficial but partitioning is the first step for cost reduction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a materialized view of common aggregations.
Why it's wrong here
Materialized views help with repeated queries but do not reduce scanning for ad-hoc queries that may not use them.
- ✗
Use clustering on the most-filtered column.
Why it's wrong here
Clustering improves performance but partitioning is more effective for cost reduction on ad-hoc queries.
- ✓
Partition the table by a date or timestamp column.
Why this is correct
Partitioning limits the data scanned per query, reducing cost and improving performance.
- ✗
Switch to on-demand pricing from slot reservations.
Why it's wrong here
On-demand pricing may be more expensive; this does not reduce data scanned.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
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