The answer is to cluster on columns used in filters and aggregations and to use SELECT * with EXCEPT to limit the columns scanned. These two actions directly improve BigQuery query performance by reducing the amount of data read from disk—clustering physically reorders data to skip irrelevant blocks during filter operations, while column pruning via SELECT * EXCEPT minimizes I/O by scanning only the necessary columns instead of the full 10 TB table. On the Google Professional Cloud Database Engineer exam, this tests your understanding of BigQuery’s architecture: clustering accelerates predicate pruning and aggregation efficiency, whereas column selection lowers both cost and execution time by reducing bytes processed. A common trap is assuming partitioning alone suffices, but clustering is critical for high-cardinality filter columns. Remember the memory tip: “Cluster to cut, select to save”—cluster on filter columns to cut scan ranges, and select only needed columns to save on data processed.
PCDE Practice Question: Define data structures and implement SQL for Business Intelligence
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses BigQuery for BI analytics. They want to improve query performance for a table with 10 TB of data. Which two actions should they take? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Limit the number of columns queried using SELECT * with EXCEPT.
Option A is correct because using SELECT * with EXCEPT limits the number of columns scanned, reducing I/O and improving query performance in BigQuery. BigQuery charges by the amount of data processed, so reading fewer columns directly lowers both cost and query execution time.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Limit the number of columns queried using SELECT * with EXCEPT.
Why this is correct
Reducing columns scanned decreases processed bytes and cost.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a wildcard table to combine multiple tables.
Why it's wrong here
Wildcard tables are for unioning tables, not for performance optimization.
✗
Partition by a column with a high granularity.
Why it's wrong here
High granularity can create many small partitions, increasing overhead.
✓
Cluster on columns used in filters and aggregations.
Why this is correct
Clustering improves data locality and reduces scan size for those columns.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a clustered column as the partition key.
Why it's wrong here
Clustering and partitioning on the same column is redundant and not recommended.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between partitioning and clustering, where candidates mistakenly think that high-granularity partitioning or using a clustered column as a partition key improves performance, when in fact it introduces overhead and defeats the purpose of each feature.
Detailed technical explanation
How to think about this question
In BigQuery, columnar storage and pruning mean that limiting columns with SELECT * EXCEPT reduces the number of blocks read from Colossus, the distributed file system. Partitioning by a high-granularity column like a timestamp with millisecond precision can exceed the recommended maximum of 4,000 partitions per table, causing query planning delays. Clustering on columns used in filters and aggregations (Option D) leverages the sort order to skip irrelevant blocks, which is especially effective when combined with partitioning on a lower-granularity column like date.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Limit the number of columns queried using SELECT * with EXCEPT. — Option A is correct because using SELECT * with EXCEPT limits the number of columns scanned, reducing I/O and improving query performance in BigQuery. BigQuery charges by the amount of data processed, so reading fewer columns directly lowers both cost and query execution time.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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