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 BI team runs a daily query on a BigQuery table 'events' partitioned by event_date. The query filters on event_date = CURRENT_DATE() and counts rows by event_type. The query is slow. Upon review, the table has 500 partitions but clustering is not set. Which action reduces query cost and latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add clustering on event_type
Adding clustering on `event_type` physically co-locates rows with the same event type within each partition. This allows BigQuery to use block-level pruning when reading data, drastically reducing the number of bytes scanned for the COUNT(*) GROUP BY query. Since the query already filters on a single partition (`event_date = CURRENT_DATE()`), the performance bottleneck is scanning all rows in that partition; clustering eliminates that overhead without changing the table's structure or retention.
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.
✗
Recreate the table with only the last 30 days of data
Why it's wrong here
Deleting old data reduces total size but doesn't improve query efficiency for the current day.
✗
Use a wildcard table for daily ingestion
Why it's wrong here
Wildcard tables don't improve performance; they might complicate queries.
Clustering on event_type organizes data by that column within each partition, speeding up count and group by.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing data volume (e.g., by deleting old partitions or using wildcards) is the primary way to fix query performance, when in fact the correct solution is to optimize data access patterns within the existing partitions using clustering.
Detailed technical explanation
How to think about this question
BigQuery clustering sorts data within each partition based on the clustering columns, creating sorted blocks that are tracked in the table's metadata. When a query includes a filter on the clustering column, BigQuery can skip entire blocks that do not contain matching values—this is block-level pruning. In this scenario, without clustering, BigQuery must scan all rows in the partition to count by `event_type`; with clustering, it reads only the blocks for each distinct event type, reducing bytes billed and improving latency. Clustering also does not incur additional storage costs, making it the most cost-effective solution.
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: Add clustering on event_type — Adding clustering on `event_type` physically co-locates rows with the same event type within each partition. This allows BigQuery to use block-level pruning when reading data, drastically reducing the number of bytes scanned for the COUNT(*) GROUP BY query. Since the query already filters on a single partition (`event_date = CURRENT_DATE()`), the performance bottleneck is scanning all rows in that partition; clustering eliminates that overhead without changing the table's structure or retention.
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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