← Anthropic Interview Insights
The core staircase part was fine, took maybe five minutes.
Start by clarifying the problem and edge cases (e.g., n=0, negative n). Then implement the function using a loop that builds each row with the correct number of items, ensuring no trailing space. For the follow-up, maintain a counter that increments across rows. Finally, write comprehensive test cases covering normal, edge, and error scenarios.
Pro tip: Demonstrate test-driven development by writing test cases before coding, and discuss time/space complexity to show efficiency awareness.
Ask about input constraints (e.g., n type, range) and expected behavior for n<=0. Confirm output format (e.g., items are asterisks or numbers).
Plan a loop from 1 to n, constructing each row with i items separated by spaces, avoiding trailing space. For the follow-up, use a running counter.
Write clean code, using string join or similar to handle spacing. For the follow-up, increment the counter for each item.
Include tests for n=0, n=1, n=3, and negative n. Verify no trailing spaces and correct item counts. For the follow-up, check consecutive integers.
State time complexity O(n^2) due to total characters printed, and space O(1) if printing directly. Mention alternative approaches if any.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints and defining the problem, then propose a baseline solution that computes exact global mode/median by centralizing all data or using a coordinator. Next, iteratively improve by using distributed algorithms that minimize communication, such as sending local summaries (e.g., histograms or quantile sketches) and merging them, or using a gossip-based approach for mode. Finally, discuss trade-offs between exactness, communication volume, and latency, and propose a hybrid solution that balances these factors.
Pro tip: Emphasize that communication is the bottleneck (1 byte/sec), so aim to reduce the number of messages and bytes sent, not just the total data. Also, consider that the mode and median have different communication complexities: median can be approximated with quantile sketches, while mode may require exact counts for frequent items.
Ask whether the result must be exact or approximate, and whether the dataset is static or streaming. Confirm the communication costs and that read_local is cheap but still limited.
Propose a simple approach: each node sends its entire local partition to a central node, which computes the global mode/median. Calculate the communication cost (total data size) and note it's likely infeasible due to slow send/recv.
Use a distributed quantile estimation algorithm (e.g., t-digest, GK sketch) where each node computes a compact sketch of its local data and sends it to a coordinator. The coordinator merges sketches to estimate the median with bounded error, drastically reducing communication.
For exact mode, use a distributed heavy-hitter algorithm (e.g., Misra-Gries, Space-Saving) where each node sends only candidate frequent items and their counts. The coordinator merges candidates and verifies counts by querying nodes if needed. For approximate mode, use count-min sketch or similar.
Compare exact vs approximate, communication vs latency, and propose a hybrid: e.g., use sketches for median and heavy-hitters for mode, possibly with multiple rounds. Also consider using a tree-based aggregation to reduce coordinator load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.