Quick Answer
The correct answer is the rolling average of purchase amounts over a 30-day window. This technique is the most appropriate because it computes a moving mean that respects the timestamp order of transactions, using a sliding window function—such as AVG() with a ROWS or RANGE frame in SQL or rolling().mean() in pandas—to ensure only the most recent 30 days of data per customer contribute to the feature. On the Salesforce AI Associate exam, this question tests your understanding of time-series feature engineering, specifically how to create a rolling average feature for an AI model that captures recent customer behavior without leaking future information. A common trap is confusing a simple overall average with a windowed calculation; the key distinction is that a rolling average dynamically updates as new timestamps enter the window. Memory tip: think of a “sliding spotlight” that only illuminates the last 30 days of data, ignoring everything older.
This AI Associate practice question tests your understanding of data for ai. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 needs to create a feature that represents the average purchase amount per customer over the last 30 days. The transactional data is timestamped. Which feature engineering technique is most appropriate?
Sum of all purchase amounts per customer
Why wrong: Sum doesn't give average.
Rolling average of purchase amounts over a 30-day window
Rolling average matches the requirement.
Count of purchases per customer
Why wrong: Count is not average.
Minimum purchase amount per customer
Why wrong: Min doesn't reflect average behavior.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Rolling average of purchase amounts over a 30-day window
Option B is correct because a rolling average over a 30-day window directly computes the average purchase amount per customer for only the most recent 30 days of transactions, which matches the requirement of a time-sensitive feature. This technique uses a sliding window function (e.g., AVG() with a ROWS or RANGE frame in SQL, or rolling().mean() in pandas) that respects the timestamp order, ensuring only relevant data contributes to the feature.
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.
Sum of all purchase amounts per customer
Why it's wrong here
Sum doesn't give average.
Rolling average of purchase amounts over a 30-day window
Why this is correct
Rolling average matches the requirement.
Related concept
Read the scenario before looking for a memorised answer.
Count of purchases per customer
Why it's wrong here
Count is not average.
Minimum purchase amount per customer
Why it's wrong here
Min doesn't reflect average behavior.
Common exam traps
Salesforce often tests the distinction between simple aggregation (like sum or count) and time-windowed aggregation, trapping candidates who overlook the 'over the last 30 days' temporal constraint and choose a static aggregate instead.
Detailed technical explanation
Under the hood, a rolling average over a 30-day window requires partitioning data by customer and ordering by timestamp, then applying a window function with a frame clause like RANGE BETWEEN INTERVAL '30' DAY PRECEDING AND CURRENT ROW (in SQL) or using a fixed-size window with a time-aware offset. A subtle behavior is that if a customer has no transactions in the last 30 days, the rolling average returns NULL, which must be handled (e.g., imputed with 0 or a global average) to avoid missing values in the model. In a real-world scenario, this feature is critical for churn prediction models where recent spending patterns are more indicative of customer behavior than lifetime aggregates.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: Rolling average of purchase amounts over a 30-day window — Option B is correct because a rolling average over a 30-day window directly computes the average purchase amount per customer for only the most recent 30 days of transactions, which matches the requirement of a time-sensitive feature. This technique uses a sliding window function (e.g., AVG() with a ROWS or RANGE frame in SQL, or rolling().mean() in pandas) that respects the timestamp order, ensuring only relevant data contributes to the feature.
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
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