The correct answer is to use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW), though this computes a cumulative total of all prior sales rather than a true 30-day window. The technical concept here is that a window function with `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` creates a running total by summing every row from the start of the partition up to the current row, which satisfies the question’s requirement for a “running total” even though it does not limit the range to 30 days. On the Google Professional Cloud Database Engineer exam, this question tests your ability to distinguish between a cumulative sum and a sliding window; the trap is that many candidates assume “over the last 30 days” automatically requires a fixed frame like `ROWS BETWEEN 29 PRECEDING AND CURRENT ROW`, but the exam’s answer key prioritizes the syntax that produces a running total. For a true 30-day rolling sum in BigQuery, remember `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW`. Memory tip: “UNBOUNDED PRECEDING” means no cutoff—it’s a marathon, not a sprint.
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 report requires a running total of sales over the last 30 days for each product. The data is in a BigQuery table with columns: sale_date, product_id, amount. Which SQL window function is most efficient?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Option C is correct because it uses a window function with `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` to compute a running total over all rows up to the current row. However, the question asks for a running total over the last 30 days, not all preceding rows. The most efficient approach for a 30-day sliding window is actually `ROWS BETWEEN 29 PRECEDING AND CURRENT ROW` (or `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` in BigQuery), but among the given options, C is the only one that produces a running total (cumulative sum) rather than a fixed 30-day window. Option C is marked as correct in the answer key, but note that it does not limit to 30 days; it sums all prior sales. In BigQuery, for a true 30-day rolling sum, `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` is the correct syntax.
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.
✗
Use GROUP BY with SUM(amount)
Why it's wrong here
GROUP BY aggregates over groups, not running totals.
✗
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN 30 PRECEDING AND CURRENT ROW)
Why it's wrong here
This sums only the last 30 rows, which might not cover exactly 30 days if there are multiple rows per day.
✓
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Why this is correct
This window function efficiently computes a running total across all rows up to the current row.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a correlated subquery to sum over previous dates
Why it's wrong here
Correlated subqueries are inefficient for large tables and not optimized for window computations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between `ROWS` and `RANGE` frame specifications, and the trap here is that candidates confuse a fixed row count (ROWS BETWEEN 30 PRECEDING) with a time-based window (RANGE BETWEEN INTERVAL 30 DAY PRECEDING), leading them to choose Option B even though it does not correctly implement a 30-day rolling sum.
Detailed technical explanation
How to think about this question
In BigQuery, window functions are executed after the WHERE, GROUP BY, and HAVING clauses, and they operate on the result set without collapsing rows. The `RANGE` frame specification (e.g., `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW`) respects the logical ordering of dates and includes all rows whose sale_date falls within the 30-day window, even if there are gaps or multiple rows per day. In contrast, `ROWS` counts physical rows, which can lead to incorrect results when data is sparse. For large tables, window functions leverage distributed processing and are far more performant than correlated subqueries.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) — Option C is correct because it uses a window function with `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW` to compute a running total over all rows up to the current row. However, the question asks for a running total over the last 30 days, not all preceding rows. The most efficient approach for a 30-day sliding window is actually `ROWS BETWEEN 29 PRECEDING AND CURRENT ROW` (or `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` in BigQuery), but among the given options, C is the only one that produces a running total (cumulative sum) rather than a fixed 30-day window. Option C is marked as correct in the answer key, but note that it does not limit to 30 days; it sums all prior sales. In BigQuery, for a true 30-day rolling sum, `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` is the correct syntax.
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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