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Yes, it can also be solved using set operations such as INTERSECT, NOT IN, or JOIN between filtered customer lists. However, conditional aggregation is usually more concise and performs well on large datasets.
Yes, similar SQL filtering and aggregation problems are common in FAANG and other data-focused interviews. They test your understanding of grouping, conditional aggregation, and filtering datasets efficiently.
The optimal approach is using SQL conditional aggregation with GROUP BY. By counting or summing conditions for products A, B, and C per customer, you can filter customers who bought A and B but exclude those who bought C efficiently.
The key concept is aggregation using GROUP BY along with conditional expressions such as CASE WHEN. This allows you to check the presence or absence of specific product purchases for each customer in a single pass.