- A
Use GROUP BY with SUM(amount)
Why wrong: Incorrect. GROUP BY with SUM(amount) aggregates over the entire dataset or per group, not per row, and does not produce a running total.
- B
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN 30 PRECEDING AND CURRENT ROW)
Why wrong: Incorrect. This window frame sums exactly 30 rows (including current) regardless of date gaps, so it does not correctly implement a 30-day rolling sum. Also, it would miss sales if there are fewer than 30 rows in the window.
- C
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Correct. This computes a cumulative running total over all preceding rows, which is the intended meaning of 'running total' in many contexts. While it does not limit to 30 days, it is the most efficient window function among the choices and is commonly accepted for BI reports.
- D
Use a correlated subquery to sum over previous dates
Why wrong: Incorrect. A correlated subquery would be inefficient and cumbersome compared to a window function, and it does not leverage BigQuery's optimized windowed aggregation.
How to Compute a Running Total Over the Last 30 Days with SUM OVER
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?
Quick Answer
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.
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
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Option C is correct because it is the only option that implements a running total (cumulative sum) using a window function with `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`, which adds each row's amount to the sum of all previous rows. Although the question asks for a 30-day rolling sum, none of the provided options correctly implement a date-based rolling window (which would require `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` in BigQuery). Option B uses `ROWS BETWEEN 30 PRECEDING AND CURRENT ROW`, which counts exactly 30 rows regardless of date gaps or multiple sales per day, making it incorrect for a time-based running total. Options A and D do not use window functions and are inefficient or incorrect. Therefore, C is the most efficient and technically correct choice for a running total among the given options.
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
Incorrect. GROUP BY with SUM(amount) aggregates over the entire dataset or per group, not per row, and does not produce a running total.
- ✗
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN 30 PRECEDING AND CURRENT ROW)
Why it's wrong here
Incorrect. This window frame sums exactly 30 rows (including current) regardless of date gaps, so it does not correctly implement a 30-day rolling sum. Also, it would miss sales if there are fewer than 30 rows in the window.
- ✓
Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Why this is correct
Correct. This computes a cumulative running total over all preceding rows, which is the intended meaning of 'running total' in many contexts. While it does not limit to 30 days, it is the most efficient window function among the choices and is commonly accepted for BI reports.
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
Incorrect. A correlated subquery would be inefficient and cumbersome compared to a window function, and it does not leverage BigQuery's optimized windowed aggregation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud tests the distinction between `ROWS` and `RANGE` frame specifications. The trap here is that candidates may choose option B assuming it implements a 30-day rolling sum, but `ROWS BETWEEN 30 PRECEDING` counts exactly 30 rows, not a date range. Option C, while not a 30-day window, is the only correct window function for a cumulative running total among the given options. A true time-based rolling sum would require `RANGE` with an interval, which is not listed.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this PCDE question test?
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 is the only option that implements a running total (cumulative sum) using a window function with `ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW`, which adds each row's amount to the sum of all previous rows. Although the question asks for a 30-day rolling sum, none of the provided options correctly implement a date-based rolling window (which would require `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` in BigQuery). Option B uses `ROWS BETWEEN 30 PRECEDING AND CURRENT ROW`, which counts exactly 30 rows regardless of date gaps or multiple sales per day, making it incorrect for a time-based running total. Options A and D do not use window functions and are inefficient or incorrect. Therefore, C is the most efficient and technically correct choice for a running total among the given options.
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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Last reviewed: Jun 30, 2026
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