Round 1: Coding 1
Questions:
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Problem 1:
- Built solution from the ground up, suboptimal -> optimal.
- Interviewer agreed it was correct.
- Discussed edge cases during coding and cleaned them up immediately.
- Ran dry run on a couple of test cases.
- Asked about time complexity; candidate provided an educated guess, but interviewer seemed unsatisfied.
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Problem 2:
- Found the optimal solution fairly quickly but overengineered initially.
- Interviewer hinted at the unnecessary complexity; candidate scaled down the solution for the given constraints.
- Successfully ran a dry run and discussed edge cases.
- Provided correct time/space complexity.
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Follow-up: Had 5 minutes to ask questions about Meta.
Candidate's Approach
- For Problem 1, built the solution iteratively, addressing edge cases as they arose.
- For Problem 2, recognized the need to simplify the solution after initial overengineering, successfully adapting to constraints.
Interviewer's Feedback
- Interviewer agreed with the correctness of the solutions but expressed some dissatisfaction with the candidate's confidence in discussing time complexity.
Round 2: Behavioral
Questions:
- Interviewer outlined the structure of the behavioral interview, emphasizing time management.
- Struggled with the first "Tell me a time when..." question; interviewer felt it lacked sufficient depth regarding sacrifice.
- Intensive interview with multiple follow-up questions challenging design choices made in intern projects.
- Successfully answered 2-3 other behavioral questions with slightly better results than the first.
Candidate's Approach
- Attempted to reference Meta's core values during responses.
- Defended design decisions but felt answers could have been more concise.
- Experienced difficulty reading the interviewer's satisfaction level.
Interviewer's Feedback
- Interviewer challenged design decisions and provided mixed feedback, summarizing takeaways after each question.
Round 3: Coding 2
Questions:
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Problem 1:
- Recognized the problem and derived the best solution clearly.
- Addressed edge cases as they were brought up, leading to a satisfactory solution.
- Provided correct time/space complexity.
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Problem 2:
- Performed well, recognizing core concepts early and building the optimal solution.
- Caught all edge cases, leading to a happy interviewer.
- Modular design facilitated an easy follow-up.
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Follow-up: Engaged in a discussion about Meta, new grad experiences, and team culture.
Candidate's Approach
- For both problems, the candidate demonstrated clarity in thought and execution, addressing edge cases proactively and ensuring modularity in design.
Interviewer's Feedback
- Interviewer was very pleased with the solutions provided and the candidate's approach to edge cases.
Overall Assessment
- The candidate is seeking feedback on their odds of success, particularly concerned about the behavioral round's impact.
- They request honest feedback regarding their performance.