Mid standard

• Connect the assumptions, method and result: I confirm each interval has start less than end and all share a room and time basis because the merge rule depends on that contract. Half-open intervals mean an end equal to the next start does not overlap. I define empty input as empty output.
• Test the consequential edge case: For [1,8), [2,3), [8,10) I keep [1,8) and [8,10), because the nested interval cannot shrink the end and adjacency is not overlap. I also test duplicates, one interval and empty input, and reject reversed endpoints.
• Explain an owned validation or operating check: Sorting costs O(n log n) and the scan is O(n); output may need O(n) space because every interval could be separate. If input is guaranteed sorted I can stream the merge, but I still validate ordering or rely on an explicit contract.

Lessons to prepare

Mid standard

• Connect the assumptions, method and result: I clarify whether the window uses arrival time or event time and whether an ID can be reused because those choices determine retention. I cannot promise exact duplicate detection forever with bounded memory and unbounded distinct IDs.
• Test the consequential edge case: I test duplicate arrival at the boundary, out-of-order event time and two threads seeing an unseen id together. I need atomic check-and-record because separate operations can both accept one event; I define whether equality at expiry is allowed.
• Explain an owned validation or operating check: I measure state growth and choose an explicit overload policy because evicting unexpired IDs weakens the guarantee. I persist state or state that a restart resets protection, and I coordinate the side effect with idempotent delivery instead of claiming a map solves exactly-once effects.

Lessons to prepare

Mid standard

• Connect the assumptions, method and result: I ask which case, whitespace and Unicode rules apply because normalization changes identity. I define k equal to zero as an empty output, reject negative k and return every distinct term when k exceeds their count.
• Test the consequential edge case: A size-k heap can reduce sorting work when k is small relative to distinct terms, but its comparator must put the worst retained entry at the root. I sort the retained entries for final output because heap order alone is not the requested result.
• Explain an owned validation or operating check: I test equal counts, repeated whitespace, empty input, k zero and k larger than the distinct count because those cases expose identity and boundary defects. Counting needs space proportional to distinct terms; an approximate sketch changes the guarantee and must be labeled.

Lessons to prepare

Mid standard

• Use csv.DictReader, Decimal and bounded error evidence, keeping rejected records distinct from zero-valued records.
• I test quoted commas, a negative refund, blank region and amounts 0.10 plus 0.20 because splitting on commas or using unchecked floats can corrupt the result. A malformed amount increments rejected rows while valid rows still total exactly 0.30.
• Memory grows with distinct regions and the bounded error sample rather than total rows because each accepted row updates existing group state. If region keys are unbounded, I spill or partition aggregates; I publish only after reconciling accepted plus rejected rows to the input count.

Lessons to prepare

Mid standard

• Track the immediately previous event per user, use deterministic ordering and test the exact 30-minute boundary.
• The first three events belong to one session because each consecutive gap is 30, while minute 91 starts the second with a gap of 31. I test unsorted input, duplicate delivery and empty input to catch boundary and state-reset mistakes.
• The general sort costs O(n log n) with deduplication state proportional to distinct IDs; an explicitly sorted input can support a linear scan because old user groups no longer need session state. An out-of-order stream needs a lateness and finalization policy before counts can be final.

Lessons to prepare

Mid standard

• Use a single-pass dictionary without mutating inputs and test out-of-order versions, ties and tombstones.
• I test an active version 4, deleted version 5 and late active version 3 because the final output must omit that product. I add equal-version events with different event IDs, an unchanged input assertion and empty input to test tie-breaking and mutation.
• The scan is O(n) with one stored winner per distinct product because each record needs one dictionary comparison. Sorting returned products adds O(u log u) for u winners, and retaining tombstones across incremental batches is necessary so later old updates cannot resurrect deleted products.

Lessons to prepare