← Anthropic Interview Insights
The official prompt was way shorter than what I'd seen discussed online.
Start by clarifying requirements: file size, number of files, memory constraints, and whether exact duplicates only or near-duplicates. Then propose a multi-stage approach: group by size, then hash file contents (e.g., SHA-256) to identify exact duplicates, and discuss trade-offs between full hashing and partial hashing for performance. Finally, outline how to handle duplicates (delete, move, or report) and address edge cases like large files and concurrency.
Pro tip: Mention that you would use a fast non-cryptographic hash (like xxHash) for initial grouping and a cryptographic hash (like SHA-256) for final verification to balance speed and collision resistance. Also, discuss the importance of not loading entire files into memory—stream them in chunks.
Ask about the scale (number of files, total size), memory limits, whether duplicates should be deleted or just reported, and if near-duplicates matter. This ensures the solution fits the context.
Propose grouping files by size first, then hashing contents of same-size files. Use a fast hash for initial grouping and a cryptographic hash for final confirmation to avoid false positives.
Compare full-file hashing vs. partial hashing (e.g., first/last few KB) for performance. Discuss memory usage, I/O bottlenecks, and parallelization opportunities.
Decide on actions (delete, move, report) and ensure safe handling (e.g., keep one copy). Address edge cases: empty files, symlinks, permission issues, and concurrent modifications.
Sketch code structure (e.g., using a dictionary mapping hash to file paths) and mention testing with unit tests and large-scale simulations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Came right after coding, didn't have much time to shift gears mentally.
Start by acknowledging the importance of a systematic, data-driven approach to diagnosing performance issues. Outline a structured process that begins with understanding the symptoms and scope, then moves to gathering data from monitoring tools, forming hypotheses, and testing them in a controlled manner. Emphasize collaboration with relevant teams and documenting findings for future reference.
Pro tip: Always correlate metrics across different layers (e.g., application, database, infrastructure) to avoid tunnel vision and to identify the true root cause. Also, consider recent changes (deployments, config changes) as potential triggers.
Clarify what 'slow' means: latency, throughput, error rates? Determine when it started, which components are affected, and whether it's intermittent or constant.
Use APM tools, metrics dashboards, and logs to identify anomalies. Look at CPU, memory, I/O, network, and application-level metrics like response times and error rates.
Based on data, hypothesize potential bottlenecks (e.g., database queries, external API calls, memory leaks). Test each hypothesis by isolating components or using profiling tools.
Once the bottleneck is identified, apply a targeted fix (e.g., optimize query, add caching, scale resources). Verify improvement with metrics and ensure no regressions.
Record the root cause, solution, and lessons learned. Update runbooks, add alerts, or implement automated scaling to prevent similar issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They explicitly nudged me toward system design thinking here.
Acknowledge the fundamental shift from single-node to distributed systems, then systematically address key challenges like consistency, fault tolerance, and scalability. Focus on trade-offs and how the design would adapt, rather than listing technologies.
Pro tip: Emphasize that distributed systems introduce partial failures and network partitions, so you must design for failure from the start. Show awareness of CAP theorem and the importance of idempotency and retries.
Discuss how distribution introduces network latency, partial failures, and coordination overhead. Mention that assumptions valid in a single node (e.g., shared memory, atomic operations) no longer hold.
Explain how you would choose between strong and eventual consistency based on requirements. Reference CAP theorem and discuss trade-offs between consistency, availability, and partition tolerance.
Describe strategies like sharding, replication, and load balancing to scale horizontally. Discuss how to handle failures with techniques like quorum, leader election, and graceful degradation.
Cover data partitioning, replication strategies, and consistency models. Mention coordination services (e.g., ZooKeeper, etcd) for leader election and configuration management.
Highlight the need for distributed tracing, centralized logging, and monitoring to debug and maintain the system. Discuss deployment and upgrade strategies in a distributed environment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.