I recently attended an interview for TikTok, below is my interview experience and experience sharing, including TikTok Interview Various aspects and provide real-world solutions that will hopefully help.
TikTok Interview Details
1. Coding
recurrence of the title:
Given a two-dimensional array representation of an image, starting pixel points and new color values, implement the "flood fill" algorithm.
optimal solution:
from collections import deque
def floodFill(image, sr, sc, newColor):
original = image[sr][sc]
if original == newColor.
return image
m, n = len(image), len(image[0])
queue = deque([(sr, sc)])
while queue.
x, y = queue.popleft()
image[x][y] = newColor
for dx, dy in [(0,1),(1,0),(0,-1),(-1,0)]: nx, ny = x + x + dy.
nx, ny = x + dx, y + dy
if 0 <= nx < m and 0 <= ny < n and image[nx][ny] == original.
queue.append((nx, ny))
return imageInterview Tips:
- Complexity analysis: Explicitly state the O(mn) time complexity and worst-case O(mn) space complexity of BFS
- Boundary processing: Special discussion of the case where the original color is equal to the new color
- the problem of variants: Preparation of the answer to the question "How to optimize large image processing" (chunking/parallel computing)
2. Recommendation system design (LLM applications)
system architecture:
graph TB
A [user behavior data] --> B [real-time feature engineering]
A --> C [offline feature storage]
B --> D [LLM feature enhancement]
C --> D
D --> E [multimodal fusion layer]
E --> F [depth-ordered modeling]
F --> G [AB testbed]
G --> H [online services]LLM Integration Key Points:
- feature enhancement: Extracting semantic features of text/video using LLMs
- cold start: Using LLM to generate user interest profiles
- interpretability: Generate testimonials through LLM
High Frequency Question Answer (HFQA):
- "How to deal with data bias?" → Answer: reducing exposure bias through adversarial learning
- "Model update strategy?" → Answer: online learning + full weekly updates
Three key features of TikTok interviews
- algorithmic question preference:
- High frequency: graph algorithms (especially DFS/BFS)
- FAQ: dynamic programming (stock trading variant)
- New trend: multi-threaded topics
- System design focus:
- Short video recommendation system
- Global CDN Design
- Social Graph Storage
- Behavioral Interview Traps:
- "How do you cope with emergency on-line pressures?"
- "Collaborative experiences across time zones?"
Professional Interview Assistance Program
1. Algorithmic sprint training
- TikTok High Frequency Question Bank(50+ selected topics)
- Code style optimization(in accordance with Google Style Guide)
- Whiteboard coding simulation(with real-time feedback)
2. System design depth preparation
title TikTok System Design Test Point Distribution
"Recommender System" : 45
"Storage System" : 25
"Distributed Computing" : 20
"Other" : 103. Behavioral interview polishing
- The STAR-L Rule: Situation-Task-Action-Result-Learning
- 20 Story Templates: Covering all leadership principles
- Stress Test Simulation: Emergency response training
Successful Cases
contextsMs. L., a non-local student with 2 years of back-end experience.
Service Options: Full Escort Package
results-based:
- Optimal solutions to all algorithmic problems
- System design rated "very well designed"
- Getting a Level 2-2 offer (total package over last job +180%)
Programehelp helps you with interviews
Programehelp TeamWe know how difficult it is for you to find a job, especially when you have to face a difficult interview like TikTokh. Our professional interview service and comprehensive interview assistance is to help you avoid detours. Whether it's the headache of OA or the subsequent VO interview, we have rich experience and professional skills to provide you with all-round support.
If you want to get more information about TikTok interview Cases, feel free toContact us!
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