TikTok 2026 Intern CodeSignal OA: Passed in One Go! Detailed Solutions & Pitfalls Guide

I just completed the TikTok 2026 Intern CodeSignal OA — 4 problems in 50 minutes, passed on the first try, and even had extra time to review. As someone who's been through it, I must say: the OA looks long at first glance, but the core logic is very clear. Once you find the right approach, there’s no need to panic. Today, I’m sharing the full problem-solving strategies, prep tips, and useful tools for anyone aiming for big tech internships.

OA Quick Overview

Platform: CodeSignal (native problem style, no reliance on TikTok-specific past problems)
Number of problems: 4
Time: 50 minutes
Difficulty: Medium to medium-high (focus on clear thinking + fast implementation)
Key point: The problem descriptions are long but straightforward — understanding the logic is enough to solve them quickly.

Problem Breakdown & Optimal Solutions

T1: Maximum Rating-to-Price Ratio (Easy / “Free points” problem)

Problem: Given two arrays (product ratings and product prices), find the product with the highest "rating ÷ price" ratio. If multiple products tie, return the one with the smallest index.

Core Idea: Linear scan + avoid precision issues

Iterate over all products. Avoid direct division (floating-point precision issues).
Compare using cross multiplication: for products i and j, check if rating[i] * price[j] > rating[j] * price[i]. Integer operations guarantee no precision errors.
Keep track of the current maximum ratio and its index — one pass is enough.

Suggested Time: 8–10 min (solve it fast, don’t overthink)

T2: Car Ride to the Destination (Greedy Algorithm)

Problem: Starting at position 0, go to the destination with several cars along the way. Each car can drive up to 10 meters. You can use a car when you reach its position. Find the total distance traveled using cars.

Core Idea: Sort + greedy selection

Sort cars by position in ascending order.
Track current position, iterate over each car: use it only if car.position >= current_position.
Each time, the car drives up to min(destination, car.position + 10) and add to the traveled distance.
Stop once current_position >= destination.

Suggested Time: 10–12 min (sorting + logic is straightforward; pay attention to boundary conditions)

T3: Battery Rotation (Simulation + Priority Management)

Problem: Phone needs t minutes of usage. You have n fully charged spare batteries, each with different capacity and charging speed. When a battery runs out, it can be recharged. Batteries are used in order, looping if needed. Find the minimum number of initially full batteries to last t minutes. Return failure if impossible.

Core Idea: Simulate usage + track recharge times

Use batteries in order, track “next full charge time” (initially 0 for fully charged).
When a battery runs out, pick the next available fully charged battery in sequence.
Update usage time and calculate when this battery will be fully recharged (current_time + charging_time).
If no fully charged battery is available at any point, return failure.

Suggested Time: 15–18 min (careful simulation, don’t miss recharge time)

T4: Dynamic Longest Consecutive Interval (Hash Map Merge)

Problem: Dynamically add positions. After each insertion, return the length of the longest consecutive interval.

Core Idea: Hash maps to maintain interval endpoints + global max

Use two hash maps: leftMap (left endpoint → interval length) and rightMap (right endpoint → interval length).
When inserting a new position x:
- Check if x-1 (left neighbor) or x+1 (right neighbor) exists.
- If both exist: merge three intervals, update new left and right endpoints with the merged length.
- If only one exists: merge two intervals, update endpoints accordingly.
- If neither exists: create a new interval of length 1.
Update global max length after each insertion and return it.

Suggested Time: 12–15 min (hash map operations O(1), key is to understand merging logic)

Prep & Pitfalls Tips

Time allocation: Don’t get stuck on one problem! Solve T1 & T2 first, then tackle T3 & T4. Try to limit each problem to ≤15 min, leaving 5–10 min for review.
Key practice focus: No need to memorize TikTok-specific questions. Focus on native CodeSignal problems, especially these four types: greedy, simulation, dynamic interval merging, and hash map applications.
Simulation training: Do 50-min full-length timed practices to get used to high-pressure environments.
Precision & boundary: Watch for floating-point precision in T1, destination logic in T2, and interval merging in T4. Always test edge cases.

Useful Tools When Stuck

If you get stuck or feel anxious during a timed OA, or just want to guarantee a 100% pass, try Programhelp’s OA assistance service — personally tested and reliable.

Platform coverage: CodeSignal, HackerRank, Niuke, and other big-tech OA platforms
Advantages: Remote human assistance without leaving traces; only charged when all test cases pass; full refund if not passed
Applicable scenarios: Intern / campus OAs, timed exams, especially useful if you want a one-shot pass without repeated trial and error.

TikTok’s OA isn’t hard — clear thinking, speed, and attention to details can get you through in one go. Good luck securing your dream internship! Feel free to comment if you want full code examples or further problem discussion.