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Meta·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Meta DS coding screen, one algorithmic problem the whole time. Clean enough premise but there are a few edge cases that'll trip you up if you're not careful about how you group things.

Questions Asked (1)

Q1

A student can read books from a summer program, each with a category and a point value. You can pick at most 3 books, but no two can share a category. Write a function that returns the maximum total points achievable.

Algorithms & Data Structures
Author's notes

My first instinct was to just sort everything by points descending and grab the top 3, which obviously breaks the moment you have duplicate categories.

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AI HintsAI Generated

Suggested Approach

Clarify the problem constraints and edge cases, then propose an efficient algorithm that groups books by category and selects the top books from distinct categories. Discuss time and space complexity, and consider alternative approaches like dynamic programming or greedy with sorting.

Pro tip: Demonstrate awareness of real-world data science applications by mentioning how this problem relates to feature selection or recommendation systems, and always test with edge cases like fewer than 3 categories or negative points.

1. Understand the problem

Restate the problem in your own words, ask clarifying questions about input format, constraints (e.g., number of books, point range), and edge cases (e.g., fewer than 3 categories, ties).

2. Brainstorm approaches

Consider brute force (check all combinations of up to 3 books from distinct categories) and optimized approaches like sorting books by points within each category and then selecting the best combination across categories.

3. Choose and explain algorithm

Select an efficient approach: group books by category, sort each group descending by points, then either use a priority queue to pick top books from distinct categories or dynamic programming to maximize points with at most 3 books from distinct categories.

4. Analyze complexity and edge cases

State time and space complexity (e.g., O(n log n) for sorting, O(n) space). Discuss handling cases with fewer than 3 categories, negative points (if allowed), and ties.

5. Test with examples

Walk through a small example to verify correctness, including edge cases, and optionally write pseudocode or actual code if requested.

Key Points to Mention

  • Grouping books by category and sorting within each category to efficiently find top candidates.
  • Using a max-heap or priority queue to select the best books from distinct categories.
  • Dynamic programming approach: dp[i][j] = max points using first i categories and picking j books.
  • Time complexity: O(n log n) due to sorting, or O(n) with selection algorithms; space complexity O(n).
  • Edge cases: fewer than 3 categories, negative points, empty input, and ties in points.
  • Real-world relevance: similar to feature selection or recommendation systems where diversity constraints exist.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.