Unit 5: Sets, Maps, and Hash Tables

Use a map from value to index to find two values that add to a target in one pass.

Create a key from sorted characters or counts so words with the same letters are grouped together.

Count characters, then scan in original order to find the first character that appears once.

Determine whether the same value appears twice within k positions using a moving set.

Use a set to start runs only at values that have no predecessor.

Track previous prefix sums so each new sum can count matching earlier sums.

Count frequencies and choose an efficient structure to return the k most common values.

Design get and put operations that update recent-use order and remove the least recent item.

Find repeated fixed-length strings by storing seen codes and duplicates separately.

Decide whether two response lists have the same counts for every category.

Given values from 1 to n with one missing and one repeated, identify both and explain tradeoffs.

Build a map from simplified word forms to possible dictionary words and discuss collision-like ambiguity.

Write put, get, remove, and resize logic using buckets that store linked entries.

Simulate inserts into a hash table and report how collision counts change as load factor grows.

Explain how changing fields used in hashCode or equals after insertion can break map lookup.

Given several lists, return values that appear in every list while avoiding duplicate output.

Given document titles and words, map each word to the set of documents where it appears.

Given login and logout events, maintain the set of active users and report the peak active count.

Check whether rows, columns, and small regions contain no repeated nonzero values.

Given point coordinates, group pairs by slope or difference while avoiding double counting.

Track recent request times per user and decide whether each new request is allowed.

Use a map from student to another map of assignment scores, then compute each student latest average.

Preprocess word positions so repeated distance queries can be answered efficiently.

Given a map where each key points to another key, detect whether following links enters a cycle.

Replace words using a synonym map while preserving words not found in the map.

For each window of size k, report how many distinct values appear.

Use a map of last-seen positions to maintain a window with unique characters.

Create maps by student ID, course name, and grade level, then explain which query each map supports.

Given a hash function that maps many strings to the same bucket, describe the performance and propose a better approach.

Create a staged assignment with frequency maps, indexing, collision analysis, and code review.