Google PCA Practice Question: Analysing and Optimising Technical and Business Processes
A company has a BigQuery dataset with a growing fact table (500 million rows, added daily). Queries that filter on a date column and group by a product ID are slow. The team wants to optimise query performance without increasing slot costs. Which two actions should they take? (Choose TWO.)
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 by the product ID column
Partitioning by date and clustering by product ID can significantly improve query performance: partitioning prunes scans to relevant days, and clustering co-locates rows with similar product IDs. Materialised views pre-aggregate data but increase storage costs. SELECT * is inefficient and should be avoided. Slots pricing does not help performance without changing configuration.
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 by the product ID column
Why this is correct
Clustering co-locates rows with similar product IDs, reducing the amount of data scanned for GROUP BY queries.
- ✗
Switch to BigQuery Editions with Autoscaling slots
Why it's wrong here
Changing slot pricing models does not inherently improve query performance; it may increase cost without performance gain.
- ✗
Use SELECT * only when necessary
Why it's wrong here
Avoiding SELECT * is a best practice but does not address the performance issue for the described query.
- ✗
Create a materialised view that pre-aggregates the data
Why it's wrong here
Materialised views can improve performance but may increase storage costs. The question asks to optimise without increasing slot costs, but materialised views do not directly affect slot costs; they use storage. The question's focus is on performance optimisation without increasing slot costs, and materialised views are not strictly necessary if partitioning and clustering are applied.
- ✓
Partition the table by the date column
Why this is correct
Partitioning allows BigQuery to scan only the relevant partitions (e.g., recent days) rather than the entire table.
Go deeper
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Key term
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A CAN (Controller Area Network) is a robust vehicle bus standard designed to allow microcontrollers and devices to communicate with each other without a host computer.
Key term
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Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Written by Johnson Ajibi, MSc IT Security
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