Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
A team uses BigQuery for real-time dashboarding. Queries on a large table take over 30 seconds. The table is date-partitioned and has high cardinality in the `user_id` column. Which optimization is MOST likely to reduce query latency?
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
✓
Cluster the table on user_id
Clustering sorts data within partitions based on the clustering columns, which significantly improves query performance for filter and aggregation on those columns. `user_id` is high cardinality, so clustering on it helps. Partitioning alone is already done. Avoiding `SELECT *` helps but is not the most impactful here. Materialized views would help if the same aggregation is reused. Increasing slots costs money.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cluster the table on user_id
Why this is correct
Clustering on a high-cardinality column like user_id reduces the amount of data scanned for queries filtering on user_id.
- ✗
Use materialized views for all queries
Why it's wrong here
Materialized views are useful for repeated aggregations, but not for ad-hoc dashboard queries with varying filters.
- ✗
Avoid using SELECT * in queries
Why it's wrong here
While good practice, it's a general optimization; clustering has bigger impact for this scenario.
- ✗
Increase BigQuery slot reservations
Why it's wrong here
More slots reduce query execution time but at higher cost; optimization should start with table design.
Go deeper
Related to this question
Learn chapter
Cloud SQL and Managed Data Stores
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
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