TikTok vs LinkedIn: Interview Question Comparison

When preparing for technical interviews, company-specific question lists provide targeted practice. TikTok and LinkedIn both test core data structures and algorithms, but their question banks differ significantly in size, difficulty distribution, and focus areas. Understanding these differences helps you allocate your study time efficiently.

Question Volume and Difficulty

TikTok's list is substantially larger and more challenging. With 383 questions, it more than doubles LinkedIn's 180. The difficulty distribution also skews harder.

TikTok (383 total)

• Easy: 42 (11%)
• Medium: 260 (68%)
• Hard: 81 (21%)

LinkedIn (180 total)

• Easy: 26 (14%)
• Medium: 117 (65%)
• Hard: 37 (21%)

While the percentage of Hard questions is similar, TikTok's sheer volume means you'll face nearly twice as many Hard problems in absolute terms (81 vs. 37). The massive pool of Medium questions (260) suggests TikTok interviews heavily emphasize this tier, requiring both speed and accuracy on complex problem-solving.

Topic Overlap

Both companies emphasize Array, String, and Hash Table problems. These are foundational for assessing coding fluency and logical thinking. Mastery here is non-negotiable for either interview.

The key divergence is in the next layer of focus:

• TikTok prominently lists Dynamic Programming (DP). This signals an expectation to solve optimization problems involving recursion, memoization, or tabulation. DP questions are common for assessing advanced problem decomposition skills.
• LinkedIn highlights Depth-First Search (DFS). This points to a stronger emphasis on graph and tree traversal problems, which model hierarchical data and relationships—a relevant domain for a social network.

This difference influences question style. TikTok problems may require finding a minimum/maximum path or optimizing a sequence, while LinkedIn problems may involve traversing a network, searching a state space, or exploring all possibilities in a tree.

# Example DP Pattern (Fibonacci - TikTok-relevant)
def fib(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 2:
        return 1
    memo[n] = fib(n-1, memo) + fib(n-2, memo)
    return memo[n]

Which to Prepare for First

Prioritize based on your interview timeline and the job's domain.

Prepare for TikTok first if: You are interviewing for a role focused on algorithms, performance, or scalable systems. The larger question bank and DP focus demand more dedicated study time. Mastering TikTok's list will naturally cover a vast majority of LinkedIn's fundamentals (Arrays, Strings, Hash Tables), making a subsequent pivot to LinkedIn preparation faster. The high volume of Medium problems requires building stamina.

Prepare for LinkedIn first if: Your interview is sooner or the role involves graph-based data (e.g., networking features, recommendations). The smaller list is more manageable for a condensed study schedule. While you must still practice core topics, you can deepen your focus on DFS, graphs, and trees. However, transitioning to TikTok prep afterwards will require significant additional work on DP and expanding your problem count.

For a balanced approach, start with the shared core: Arrays, Strings, and Hash Tables. Then, branch based on the company: integrate DP practice for TikTok or graph traversal for LinkedIn. Always solve problems on the actual platform to simulate interview conditions.

Practice questions specifically from each company's list:

• TikTok Interview Questions
• LinkedIn Interview Questions