Instacart OA favors business scenarios, and the questions don't roll with strange techniques, but more on code readability, boundary handling, and modeling ability in e-commerce/delivery contexts. My intuition after this time: medium difficulty, but a lot of details; whoever can translate the business rules into data structures faster will be able to grab time.
Process Overview
- Platform/hours: common platforms, approx. 75-90 minutes
- Question volume: 2-3 Coding (sometimes with 1-2 SQL/data processing subtopics)
- LanguagePython/Java/C++ 任意
- angle: string/hash/sort/heap, plus a small number of intervals or greedy, close to e-commerce pricing, ordering, inventory, batch delivery, etc. scenarios
- Scoring Habits: Correctness > Complexity > Code Style/Test Coverage
Instacart OA Sample Questions
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": 5.50, "qty": 1}
{"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 = [101,101,20,20,20,20,7,7,7,7,7,5]
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 into same batch; non-overlap → new batch. Equivalent to "minimum number of resources = count of non-overlapping segments merged".
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. Note that concurrency/consistency is not required in OA, the focus is on API semantics and sequencing.
FAQ
Q: Instacart OA is business-oriented, will it be difficult?
A: It is not difficult, the core is to "proceduralize" the business rules. If the data structure is correctly chosen and the process is smooth, the score will be stable.
Q: What types of questions do I need to prepare for?
A: Hashing/sorting/heap, interval merging, basic greedy, occasional string cleaning or small SQL.
Q: What is the most stable way to handle the amount accuracy?
A: Preferred integer scores or Decimal. Do not repeat floating point operations midway through the process, and format the output in the last step.
Q: How is the boundary measured?
A: Empty inputs, extreme values, same frequency/same size side-by-side ordering, scenarios close to 0 after discount stacking, window endpoint mapping.
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