Data Abstraction Practice Test
•15 QuestionsConsidering the example provided, an Online Retail System wants to recommend products without reading every single click and message. The real-world problem is that raw data (every item viewed, time spent, cart edits, purchases, returns, and star ratings) is overwhelming for quick recommendations. The system abstracts customer data into three simplified structures: CustomerProfile (age range, region, preferred categories), PurchasePatterns (most-bought categories, average price range, repeat purchases), and FeedbackSummary (average rating by category, common return reasons). This classification reduces complexity by turning thousands of events into a few meaningful summaries. During development, programmers design algorithms that manipulate these abstractions, not the raw logs. For example, a recommendation algorithm scores items using the abstracted purchase pattern and feedback summary.
Pseudocode:
score(item) = 0
IF item.category IN PurchasePatterns.topCategories THEN score += 2
IF item.price BETWEEN PurchasePatterns.minPrice AND maxPrice THEN score += 1
IF FeedbackSummary.avgRatingitem.category >= 4 THEN score += 1
Which abstraction technique is used in the scenario to simplify data handling?
Considering the example provided, an Online Retail System wants to recommend products without reading every single click and message. The real-world problem is that raw data (every item viewed, time spent, cart edits, purchases, returns, and star ratings) is overwhelming for quick recommendations. The system abstracts customer data into three simplified structures: CustomerProfile (age range, region, preferred categories), PurchasePatterns (most-bought categories, average price range, repeat purchases), and FeedbackSummary (average rating by category, common return reasons). This classification reduces complexity by turning thousands of events into a few meaningful summaries. During development, programmers design algorithms that manipulate these abstractions, not the raw logs. For example, a recommendation algorithm scores items using the abstracted purchase pattern and feedback summary.
Pseudocode:
score(item) = 0
IF item.category IN PurchasePatterns.topCategories THEN score += 2
IF item.price BETWEEN PurchasePatterns.minPrice AND maxPrice THEN score += 1
IF FeedbackSummary.avgRatingitem.category >= 4 THEN score += 1
Which abstraction technique is used in the scenario to simplify data handling?