Start by thoroughly reading the README to understand the intended workflow and expected outputs. Then systematically inspect the filesystem, trace the data flow, and identify discrepancies between expected and actual behavior. Finally, propose and implement fixes, validating each change with tests or sample runs.
Pro tip: Always reproduce the issue first and confirm the root cause before making changes; premature fixes can mask the real problem and introduce new bugs.
Read the README and any documentation to grasp the purpose, inputs, outputs, and expected behavior of the data processing pipeline.
List all files, inspect directory structure, and read relevant scripts to map out the actual implementation and data flow.
Run the workflow with sample data to observe failures or incorrect outputs, and trace the error to its source.
Modify the code or configuration to address the underlying problem, ensuring the fix aligns with the intended design.
Test the fix with the same sample data and edge cases, then update documentation or comments to reflect changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I could see the shape of the solution pretty quickly but the input format was never clearly defined.
Start by clarifying the vague input data structure and constraints through targeted questions, then propose a solution using standard graph algorithms like BFS/DFS with appropriate modifications for constraints. Discuss trade-offs and edge cases, and be prepared to adapt your approach based on interviewer feedback.
Pro tip: Demonstrate adaptability by explicitly stating your assumptions and asking for confirmation before diving into code; this shows you can handle ambiguity and collaborate effectively.
Ask questions to understand the input format, constraints, and expected output. Confirm whether the graph is weighted, directed, and what the constraints are.
Outline a high-level algorithm (e.g., BFS, DFS, Dijkstra) that fits the clarified constraints, and explain why it's suitable.
Compare alternative approaches in terms of time/space complexity and explain your choice based on the constraints.
Identify potential edge cases (e.g., cycles, disconnected components) and explain how your solution addresses them.
Write clean code, then walk through a small example to verify correctness and discuss testing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.