Meta vs Twitter: Interview Question Comparison

When preparing for technical interviews at major tech companies, understanding the specific focus and scope of their question banks can dramatically improve your efficiency. Meta and Twitter (now X) represent two distinct patterns in their approach to coding interviews, with Meta offering a vast, comprehensive question pool and Twitter presenting a smaller, more targeted set. This comparison breaks down the key differences in volume, difficulty, topic coverage, and provides a strategic approach to preparation.

Question Volume and Difficulty

The most striking difference is scale. Meta's question bank is massive, with 1,387 total questions categorized by difficulty: 414 Easy, 762 Medium, and 211 Hard. This volume reflects Meta's broad engineering challenges and the need to assess candidates across a wide spectrum of problems. You must be prepared for algorithmic depth and endurance.

In contrast, Twitter's question bank is significantly smaller, with only 53 total questions: 8 Easy, 33 Medium, and 12 Hard. This suggests a more curated or historically focused interview process. While the number is smaller, the proportion of Medium and Hard questions is high (85%), indicating that the interviews are still rigorous and lean towards complex problem-solving.

The practical implication is clear: preparing for Meta requires grinding through a large number of problems to cover potential topics, while preparing for Twitter allows for deep, repeated practice on a known set.

Topic Overlap

Both companies emphasize core data structures. The top topics for both include Array, String, and Hash Table. This is standard for software engineering interviews, as these structures form the backbone of most algorithmic problems.

Meta's top list includes Math, highlighting a potential focus on numerical algorithms, combinatorics, or system design calculations. Twitter's list notably includes Design, which often points toward Object-Oriented Design (OOD) or system design principles being tested within their coding rounds, not just in separate sessions.

For the shared topics, the approach to problems may differ. A Hash Table problem at Meta might be part of a complex graph or DP solution, while at Twitter it might be integral to a sleek, real-time feature design. The coding patterns, however, remain consistent.

# Example: A common Hash Table pattern (Two Sum)
def two_sum(nums, target):
    seen = {}
    for i, num in enumerate(nums):
        complement = target - num
        if complement in seen:
            return [seen[complement], i]
        seen[num] = i
    return []

Which to Prepare for First

Your strategy should be dictated by the company's interview timeline and the transferability of your preparation.

Prepare for Twitter first if: You have an interview scheduled soon. You can achieve high coverage by mastering their ~50 question bank. Deeply understand each problem, its variants, and the underlying patterns. The inclusion of Design in their top topics means you should also brush up on OOD principles.

Prepare for Meta first if: You are building a long-term foundation or have a later interview date. Mastering Meta's vast question bank will inherently cover the core topics needed for Twitter and most other companies. Focus on the high-frequency topics (Array, String, Hash Table) and ensure you are comfortable with a large number of Medium-difficulty problems. The volume prepares you for the stamina required in long interview loops.

A hybrid approach is effective: use Meta's large pool for broad algorithmic practice, then switch to Twitter's specific list for targeted refinement if you have an interview there. The core skills from one directly apply to the other.

For targeted practice, visit the CodeJeet pages for Meta and Twitter.