Got the initial solution fast, maybe five minutes.
Start by restating the problem and clarifying constraints (input size, edge cases, expected output). Then present a brute-force solution, analyze its time and space complexity, and iteratively optimize by identifying bottlenecks and applying appropriate data structures or algorithmic techniques until reaching the optimal complexity.
Pro tip: Always verbalize your thought process and trade-offs between time and space; interviewers value clear reasoning over jumping to the optimal solution. If stuck, ask for hints and show willingness to learn.
Restate the problem in your own words, ask clarifying questions about input size, edge cases, and expected output format. Confirm assumptions before proceeding.
Propose a straightforward, working solution even if inefficient. Explain its logic and analyze its time and space complexity to establish a baseline.
Pinpoint the parts of the brute force solution that cause inefficiency (e.g., nested loops, repeated computations). Discuss potential improvements.
Apply optimizations step by step, such as using hash maps, sorting, two pointers, dynamic programming, or greedy approaches. After each change, re-analyze complexity and correctness.
Present the most optimized solution, explain why it's optimal (e.g., lower bound argument), and walk through test cases including edge cases to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Thought I covered everything and missed one.
Start by clarifying the problem statement and constraints, then systematically enumerate edge cases across input domains, boundaries, and special conditions. For each edge case, describe a corresponding test case and explain the expected behavior, ensuring coverage of both typical and atypical scenarios.
Pro tip: Demonstrate maturity by prioritizing edge cases based on likelihood and impact, and mention how you would automate testing to catch regressions. Also, relate edge cases to real-world scenarios AT&T might face, such as network data or large-scale systems.
Ask questions to understand the problem scope, input types, output expectations, and any constraints (e.g., time, space, data size). This ensures you focus on relevant edge cases.
Consider extremes: empty inputs, null values, minimum/maximum values, negative numbers, zero, duplicates, unsorted data, and invalid types. Also think about large inputs that might cause overflow or performance issues.
Examine boundaries like first/last elements, off-by-one errors, and special conditions such as already sorted data, all identical elements, or cyclic dependencies.
For each identified edge case, specify concrete test inputs and the expected output or behavior. Include both positive and negative tests.
Rank edge cases by importance and likelihood, and summarize how you would test them. Mention any assumptions and how you would handle untestable cases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.