Clean Coding, Requirements, and Edge Cases

What's being tested

These prompts test streaming aggregation and incremental state management for counts, plus robust hierarchical key parsing into runtime data structures. Interviewers check for correct, efficient tokenization, associative merging, memory bounds, and clear semantics when keys conflict or inputs are malformed.

Patterns & templates

• Tokenization: use re.findall(r'\w+') or split() after lower() and Unicode normalization; scanning is O(n) per chunk, watch apostrophes and emojis.

• Maintain counts in a hash map (collections.Counter or defaultdict(int)) for O(1) updates; merge counters by addition for associative reduction.

• Streaming pattern: process input as a generator/chunks, update state incrementally, periodically checkpoint/serialize state to disk to bound memory.

• For flat→nested conversion, split on delimiter (key.split('.')) and iteratively setdefault into a nested dictionary; cost O(d) per key where d is depth.

• Conflict resolution template: if a path exists as non-dict, explicitly choose either overwrite, promote value to list, or namespace (e.g., suffix _leaf) and document it.

• Use idempotent merges: design updates so reprocessing a chunk is safe (commutative + associative), or track message IDs for deduplication.

• Testing template: fuzz inputs — empty strings, trailing dots, duplicate keys, non-string values — assert deterministic behavior and memory profile.

Common pitfalls

Pitfall: Treating tokenization as trivial — failing on punctuation, contractions, or Unicode yields wrong counts and flaky tests.

Pitfall: Recursively overwriting nested structure without conflict rules — leads to type errors when a key is both a container and a leaf.

Pitfall: Keeping unbounded in-memory counts for high-cardinality streams without checkpointing or eviction, causing OOMs.

Practice these

The practice cards below cover the canonical variants — solve all of them and time yourself.