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Applied intuition·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Applied Intuition coding round focused entirely on a coupon engine design problem, split into two parts that escalated pretty fast from basic cart math to a full class design with complexity analysis and unit tests.

Questions Asked (2)

Q1

Design and implement a coupon application engine for a shopping cart. A coupon has applicable categories, minimum item count and price requirements, a percentage discount, and a flat value discount. Given one coupon and a list of cart items, compute the final price after applying the coupon once. How do you handle rounding, and in what order do you apply the percentage vs. flat discount?

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I started coding before thinking through the rounding question and that came back to bite me.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and edge cases, then outline a clear algorithm that filters applicable items, checks coupon eligibility, and applies discounts in a defined order. Discuss the trade-offs of different rounding strategies and discount application orders, and justify your choices with examples.

Pro tip: Always apply the percentage discount before the flat discount to avoid over-discounting, and use integer arithmetic (e.g., cents) to avoid floating-point precision issues.

1. Clarify Requirements and Edge Cases

Ask about coupon applicability (e.g., only to items in certain categories), whether minimums are based on all items or only applicable items, and how to handle rounding (e.g., round half up, banker's rounding).

2. Design the Algorithm

Outline steps: filter items by category, compute subtotal of applicable items, check minimum item count and price requirements, then apply percentage discount followed by flat discount (or vice versa) with justification.

3. Handle Rounding and Precision

Decide on rounding strategy (e.g., round to nearest cent) and implement using integer cents to avoid floating-point errors. Apply rounding after each discount or at the end, and explain the choice.

4. Implement and Test

Write clean code with helper functions, and test with edge cases: no applicable items, exactly meeting minimums, discounts exceeding total, and multiple rounding scenarios.

5. Discuss Trade-offs and Extensibility

Explain why you chose the discount order and rounding method, and how the design could be extended to multiple coupons or stacking rules.

Key Points to Mention

  • Applicability: only items in specified categories are eligible for the discount.
  • Minimum requirements: check both item count and price threshold on eligible items.
  • Discount order: apply percentage first, then flat, to avoid over-discounting (or justify alternative).
  • Rounding: use integer cents and round half up (or specify) to ensure consistency.
  • Edge cases: no eligible items, exactly meeting minimums, discount exceeding total (cap at zero).
  • Extensibility: design for multiple coupons, stacking rules, and different discount types.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Extend the coupon engine to support coupons that apply across multiple categories in the same cart. Walk through your data structures and class interfaces, explain the time and space complexity, and provide unit tests covering edge cases like no eligible items, overlapping categories, and zero or negative totals.

System DesignAlgorithms & Data StructuresData Modeling
Author's notes

This is where I started to feel the pressure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and defining the coupon's scope as a set of categories with an optional discount type. Then design a data model that maps cart items to categories and applies the coupon only to eligible items, ensuring correct handling of overlapping categories and edge cases. Finally, analyze complexity and write unit tests covering no eligible items, overlapping categories, and zero/negative totals.

Pro tip: Explicitly discuss how you would handle overlapping categories to avoid double-discounting, and mention that you'd validate totals to prevent negative discounts—this shows attention to real-world edge cases.

1. Clarify Requirements and Scope

Ask clarifying questions about coupon behavior: Is it a percentage or fixed amount? Does it apply to each eligible item or the subtotal of eligible items? Can it be combined with other coupons? Define what 'multiple categories' means (e.g., union of categories).

2. Design Data Structures and Interfaces

Propose a Coupon class with a set of category IDs and a discount strategy. Design a Cart class that holds items with category IDs and prices. Implement an applyCoupon method that filters eligible items, computes the discount, and ensures it doesn't exceed the eligible subtotal.

3. Analyze Time and Space Complexity

Explain that filtering items is O(n) where n is the number of cart items, and computing the discount is O(m) where m is the number of eligible items. Space complexity is O(m) for storing eligible items or O(1) if computed on the fly.

4. Write Unit Tests for Edge Cases

List test cases: no eligible items (discount zero), overlapping categories (ensure no double discount), zero total (discount zero), negative total (should not occur, but handle gracefully), and multiple items in different categories.

5. Discuss Extensibility and Trade-offs

Mention how to extend to multiple coupons, category hierarchies, or exclusions. Discuss trade-offs between precomputing category totals vs. on-the-fly calculation.

Key Points to Mention

  • Use a set for coupon categories to allow O(1) lookup and handle overlapping categories by unioning them.
  • Apply discount only to eligible items, and cap the discount at the eligible subtotal to avoid negative totals.
  • Time complexity: O(n) to filter items and O(m) to compute discount; space O(m) if storing eligible items.
  • Unit tests should cover: no eligible items, overlapping categories (e.g., item in two categories), zero total, negative total (if possible), and mixed carts.
  • Consider using the Strategy pattern for different discount types (percentage, fixed, BOGO).
  • Ensure idempotency: applying the same coupon twice should not double-discount.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.