Round 1: Coding 1
Questions:
- Specific question not provided.
- Specific question not provided.
Candidate's Approach
- For Question 1, the candidate started with a suboptimal solution but quickly derived the optimal one. They addressed edge cases as they were brought up and successfully performed a dry run. They were a bit unsure about time/space complexity but provided an educated guess.
- For Question 2, the candidate recognized the pattern early and created an optimal solution. They initially overengineered the solution but scaled it down after feedback. They successfully performed a dry run and correctly identified time/space complexity, resolving additional edge cases as well.
Interviewer's Feedback
- The interviewer did not provide specific feedback at the end but seemed satisfied with the candidate's ability to address edge cases and perform dry runs.
Round 2: Behavioral
Questions:
- "Tell me a time when..." (follow-up questions focused on personal sacrifice and design choices)
Candidate's Approach
- The candidate provided an initial answer but was prompted for more depth regarding personal sacrifice. They were given a chance to revisit this question later. The interview involved intensive questioning about design choices, where the candidate defended their decisions and explained alternatives. They incorporated Meta's core values into their responses.
Interviewer's Feedback
- The interviewer expressed dissatisfaction with the initial answer regarding personal sacrifice and asked for another example. They provided mixed feedback on the candidate's explanations of design choices but did not give clear indications of their overall impression.
Round 3: Coding 2
Questions:
- Specific question not provided.
- Specific question not provided.
Candidate's Approach
- For Question 1, the candidate found the problem straightforward and derived the optimal solution easily, addressing edge cases and performing a successful dry run. The interviewer was very satisfied with the solution.
- For Question 2, the candidate quickly recognized the pattern and explained their solution well, handling edge cases from the start and performing a proper dry run. They received positive feedback for modular code design that facilitated follow-up questions.
Interviewer's Feedback
- The interviewer was impressed with the candidate's performance and offered to connect on LinkedIn after discussing Meta's culture and tips for new grads.
Overall Summary
- The candidate felt confident about the coding rounds, having solved both sets of questions optimally. They expressed concern about the behavioral round, particularly regarding the depth of personal sacrifice in their answers. They are hopeful for a strong hire based on their performance in the coding interviews.