Quick Answer
The answer is ORDER BY date, along with AVG() and ROWS BETWEEN 6 PRECEDING AND CURRENT ROW, as the three required components. This is because a moving average with window functions in BigQuery relies on the AVG() aggregate function to compute the mean over a defined frame, while ORDER BY date establishes the chronological sequence of rows, and the ROWS clause specifies the exact window—the current row and the six preceding days—to produce the 7-day average. On the Google Professional Cloud Database Engineer exam, this tests your ability to combine window function syntax with frame specification, a common task for analytical queries on time-series data like daily sales. A frequent trap is forgetting the ORDER BY clause, which is mandatory for a meaningful moving average; without it, the window frame is undefined. Memory tip: think “AVG, ORDER, ROWS” as the three pillars of a moving average—like a tripod, each leg is essential for stability.
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.
Which THREE components are required to compute a 7-day moving average of daily sales using a window function? (Choose three.)
PARTITION BY product
Why wrong: Partitioning by product is optional for per-product average, not required for overall moving average.
WINDOW clause
Why wrong: Window can be specified inline without WINDOW clause.
AVG() function
AVG calculates the average.
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
Defines the sliding window of 7 rows.
ORDER BY date
Ordering defines the sequence for the window.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AVG() function
Option C is correct because the AVG() function is the aggregate function that computes the arithmetic mean of the sales values over the specified window frame. In a moving average calculation, AVG() is applied to the rows defined by the window frame to produce the average for each row.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
PARTITION BY product
Why it's wrong here
Partitioning by product is optional for per-product average, not required for overall moving average.
WINDOW clause
Why it's wrong here
Window can be specified inline without WINDOW clause.
AVG() function
Why this is correct
AVG calculates the average.
Related concept
Read the scenario before looking for a memorised answer.
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
Why this is correct
Defines the sliding window of 7 rows.
Related concept
Read the scenario before looking for a memorised answer.
ORDER BY date
Why this is correct
Ordering defines the sequence for the window.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the misconception that the WINDOW clause is mandatory for window functions, when in fact it is only a convenience for reusing a window specification, and the frame can be defined directly in the OVER clause.
Detailed technical explanation
A moving average is computed by defining a window frame using ROWS BETWEEN 6 PRECEDING AND CURRENT ROW, which includes exactly 7 rows (the current row and the 6 preceding rows) for each calculation. The ORDER BY date clause is essential to establish the logical order of rows within the partition; without it, the window frame would be applied to an unordered set, producing incorrect results. Under the hood, the database engine evaluates the window function for each row by scanning the defined frame in the sorted order, which can have performance implications on large datasets.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
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..
The correct answer is: AVG() function — Option C is correct because the AVG() function is the aggregate function that computes the arithmetic mean of the sales values over the specified window frame. In a moving average calculation, AVG() is applied to the rows defined by the window frame to produce the average for each row.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This PCDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDE exam.
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