Instacart OA偏业务场景化，题目不卷奇技淫巧，更看重代码可读性、边界处理、以及在电商/配送语境下的建模能力。我这次做完的直观感受：难度中等，但细节很多；谁能更快把业务规则翻译成数据结构，谁就能抢时间。
流程概览
- 平台/时长：常见平台，约 75–90 分钟
- 题量：2–3 道 Coding（有时带 1–2 道 SQL/数据处理小题）
- 语言：Python/Java/C++ 任意
- 侧重点：字符串/哈希/排序/堆，外加少量区间或贪心，贴近电商定价、订单、库存、分批配送等场景
- 评分习惯：正确性 > 复杂度 > 代码风格/测试覆盖
Instacart OA 真题示例
1) Cart Price with Coupons & Taxes
Prompt:
You are given a shopping cart: a list of items with price and quantity. You also receive a set of coupons and a tax rule.
- Each percentage coupon applies to a specific
category(e.g., “produce 10% off”) and can stack with other coupons but cannot make item price below 0. - Each fixed coupon applies to the whole cart (e.g., “$5 off total”) and is applied after percentage coupons.
- Tax is applied to the final total (after coupons).
Return the final payable amount rounded to 2 decimals.
Example:
items = [
{"sku": "A1", "category": "produce", "price": 4.00, "qty": 3},
{"sku": "B2", "category": "dairy", "price": 5.50, "qty": 1}
]
percent_coupons = [{"category":"produce", "off":10}] # 10% off produce
fixed_coupons = [{"off":5}] # $5 off cart
tax_rate = 8.5 # percent
Output: 9.41
Key points: category grouping → apply stacked percentage discounts per line → sum → subtract fixed coupons (clamp at ≥ 0) → apply tax → round.
2) Top-K Frequently Ordered Products (with tie-break)
Prompt:
Given an order history as a list of product IDs, return the top K most frequently ordered products.
- Sort by frequency (desc), then by product ID (asc) on ties.
- If fewer than K distinct products, return all.
Example:
orders = [101,101,20,20,20,7,7,7,7,5]
k = 3
Output: [7, 20, 101]
Key points: Counter/HashMap + min-heap of size K, or sort by (-freq, id). Watch large input (up to 1e5).
3) Batch Delivery Scheduling (Greedy Merge Intervals)
Prompt:
You receive N delivery windows [start, end] (minutes). A driver can deliver multiple orders in one batch if their windows overlap.
Return the minimum number of batches needed to satisfy all windows.
Example:
windows = [[10,20],[15,25],[27,35],[30,34]]
Output: 2
# Batches could be: [[10,20],[15,25]] and [[27,35],[30,34]]
Key points: Sort by start; greedily track current batch’s max-end; overlap → merge进同一批；不重叠 → 新批次。等价于“最少资源数 = 合并非重叠段的计数”。
4) Inventory Refill Simulator (Optional)
Prompt:
Maintain stock levels for SKUs with operations:
add(sku, qty)increases stock.order(sku, qty)tries to fulfill; if insufficient, fulfill what you can and return backorder amount.top_m_lowest(m)returns the SKUs with lowest stock (ascending by qty, then lexicographically).
Key points: HashMap for stock + heap or ordered structure for top_m_lowest。注意并发/一致性在 OA 不要求，重心在 API 语义与排序。
FAQ
Q：Instacart OA 偏业务，会不会很难？
A：不偏难，核心是把业务规则“程序化”。数据结构选得对、流程顺，分就稳。
Q：需要准备哪些题型？
A：哈希/排序/堆、区间合并、基础贪心，偶尔字符串清洗或小 SQL。
Q：金额精度怎么处理最稳？
A：首选整数分或 Decimal。中途不要反复浮点运算，最后一步再格式化输出。
Q：边界怎么测？
A：空输入、极值、相同频次/相同大小的并列排序、折扣叠加后接近 0 的场景、窗口端点贴边。
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