Meta vs Yandex: Interview Question Comparison

When preparing for technical interviews, understanding company-specific patterns is crucial. Meta (Facebook) and Yandex, while both being major tech companies, present distinctly different interview landscapes in terms of scale, focus, and difficulty distribution. Meta's process is highly standardized with a vast, well-documented question pool, whereas Yandex's process, while still rigorous, draws from a much more concentrated set of problems.

Question Volume and Difficulty

The most striking difference is the sheer volume of documented questions. Meta's list of ~1387 questions dwarfs Yandex's ~134. This reflects Meta's global scale, longer history of public interview tracking, and the sheer number of candidates.

The difficulty distribution also tells a story:

• Meta (E414/M762/H211): The curve is centered on Medium difficulty. This is classic FAANG structure: a few easy warm-ups, a solid majority of medium problems that form the core of the interview, and a significant number of hard problems for senior roles or particularly challenging rounds.
• Yandex (E52/M72/H10): The distribution proportionally favors Medium problems even more heavily, with Hard problems being relatively rare. This suggests their on-site interviews may focus more on solid application of core algorithms rather than extreme optimization or obscure puzzles.

This means Meta preparation requires broader exposure to a wide range of problem variations, while Yandex preparation demands deep, flawless mastery of fundamental patterns from a smaller pool.

Topic Overlap

Both companies emphasize core data structures. The top topics are nearly identical:

• Shared Top Focus: Array, String, Hash Table. These are the absolute fundamentals for both.
• Key Differentiator: Meta lists Math as a top-4 topic, indicating a notable focus on number theory, probability, or bit manipulation problems. Yandex lists Two Pointers in its top tier, highlighting a preference for efficient in-place array/string manipulation techniques.

This overlap is good news for candidates. Mastering arrays, strings, and hash tables provides a strong foundation for either company. The divergence suggests tailoring your final review: brush up on combinatorics for Meta, and practice two-pointer patterns for Yandex.

Here is a classic Two Pointers problem, highly relevant for Yandex and common at Meta:

def two_sum_sorted(numbers, target):
    left, right = 0, len(numbers) - 1
    while left < right:
        current_sum = numbers[left] + numbers[right]
        if current_sum == target:
            return [left + 1, right + 1]  # 1-indexed
        elif current_sum < target:
            left += 1
        else:
            right -= 1
    return [-1, -1]

Which to Prepare for First

Prepare for Yandex first. Its smaller, more focused question set allows you to build a strong, confident foundation in the most frequent patterns. You can achieve coverage and depth in a shorter timeframe. Success here reinforces core skills.

Then, transition to Meta. Use the broader Meta question list to stress-test your fundamentals against a huge variety of problem variations and edge cases. This will fill gaps in your knowledge and build the stamina and adaptability needed for its more extensive interview loop. The Medium-heavy focus of both companies means your core practice translates well.

Ultimately, a candidate well-prepared for Meta will likely cover the Yandex question patterns, but the reverse is less certain due to the volume difference. Start focused, then expand.

For targeted practice, visit the company pages: Meta Interview Questions and Yandex Interview Questions.