Google PCA Practice Question: Analyze and optimize technical and business processes
A company uses BigQuery for analytics. They have a large partitioned table that is queried frequently. The query performance has degraded over time. Which optimization should they try first?
⚠ Common exam trap
Google Cloud often tests the misconception that adding more slots (Option B) is the default performance fix, when in reality the first step should be to reduce data scanned through clustering or partitioning optimization.
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
✓
Apply clustering on frequently filtered columns.
Clustering on frequently filtered columns reorganizes the data within partitions based on the values of those columns, which allows BigQuery to prune blocks more effectively during queries. This directly addresses the performance degradation by reducing the amount of data scanned, without requiring additional storage or compute resources.
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 for each frequent query.
Why it's wrong here
Materialised views precompute results for specific queries, adding storage and refresh cost, and they do not fix the underlying scan inefficiency. They suit repeated identical aggregations, whereas the first step for a frequently queried partitioned table is verifying partition pruning and clustering.
- ✗
Increase the number of slots for the project.
Why it's wrong here
Adding slots increases compute capacity for the whole project, but degraded performance on a partitioned table usually stems from queries scanning partitions unnecessarily or stale statistics. Slot increases are correct for sustained workload contention, not for a single table whose pruning has regressed.
- ✓
Apply clustering on frequently filtered columns.
Why this is correct
Clustering physically co-locates rows sharing the clustered column values, so filters on those columns scan fewer blocks. On a frequently queried partitioned table, clustering the common filter columns prunes data within each partition, improving performance without restructuring partitions.
- ✗
Denormalize the table to reduce joins.
Why it's wrong here
Denormalising removes joins, but joins are not the reported bottleneck; partition scanning is. Denormalisation is correct when join-heavy queries dominate and storage duplication is acceptable, not when a partitioned table's query performance has degraded over time.
Go deeper
Related to this question
Learn chapter
Google Cloud Compute Options Overview
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.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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