The answer is the window function `SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)`. This construct is correct because it uses `PARTITION BY product_id` to reset the running total for each product, while `ORDER BY sale_date` defines the chronological sequence, and the explicit `ROWS` frame ensures a precise cumulative sum from the first row to the current row within each partition. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of window functions for analytical queries, a common scenario when calculating running totals per product over time. A frequent trap is omitting the frame clause or using `RANGE` instead of `ROWS`, which can produce incorrect results with duplicate dates. Remember the memory tip: “Partition to reset, Order to sequence, ROWS to be precise”—this trio guarantees an accurate running total per product in BigQuery.
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 data engineer is writing a SQL query in BigQuery to calculate the running total of sales per product over time. The table 'sales' has columns product_id, sale_date, and amount. The result must include the cumulative sum ordered by sale_date for each product. Which SQL construct should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Option B is correct because it uses a window function with a PARTITION BY clause to reset the running total per product and an ORDER BY with a ROWS frame to compute the cumulative sum over time. This is the standard SQL construct in BigQuery for calculating running totals within partitions.
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.
✗
GROUP BY product_id, sale_date with SUM(amount)
Why it's wrong here
GROUP BY collapses rows; it cannot produce a running total.
✓
SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
Why this is correct
This window function correctly computes a running total per product.
Related concept
Read the scenario before looking for a memorised answer.
✗
ROW_NUMBER() OVER (ORDER BY sale_date)
Why it's wrong here
ROW_NUMBER assigns a number, not a cumulative sum.
✗
LAG(amount, 1, 0) OVER (ORDER BY sale_date)
Why it's wrong here
LAG only returns the previous row value, not a cumulative sum.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between aggregate functions with GROUP BY and window functions with OVER, where candidates mistakenly choose GROUP BY thinking it produces a running total, but it only collapses rows.
Detailed technical explanation
How to think about this question
The window frame ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW defines the set of rows from the start of the partition up to the current row, enabling the cumulative sum. In BigQuery, this frame is the default when ORDER BY is specified in a window function, but explicitly stating it avoids ambiguity. A real-world scenario is calculating daily revenue totals per product for inventory forecasting, where the frame ensures each row includes all prior sales for that product.
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: SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) — Option B is correct because it uses a window function with a PARTITION BY clause to reset the running total per product and an ORDER BY with a ROWS frame to compute the cumulative sum over time. This is the standard SQL construct in BigQuery for calculating running totals within partitions.
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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