Technical interview prep · Interview Practice

Plan 60–90 minutes per session: 25–35 minutes reading, 15–20 minutes tracing or writing the example, 10–15 minutes attempting the exercise, then 10–20 minutes answering three or four questions from that track. Repeat a session when you cannot explain it independently. Absolute beginners can split each session across two days; the schedule is a suggestion, not a completion guarantee.

Before session 1, learn Java variables, conditionals, loops and method syntax. Java chapters 1–2 can also be read first if the examples are unfamiliar. After each five-session track, attempt its remaining questions and revisit mistakes.

Session 1 · Data structuresArrays, lists, stacks and queues
Session 2 · Data structuresHashing, equality and stable keys
Session 3 · Data structuresTrees, heaps and prefix indexes
Session 4 · Data structuresGraphs, connectivity and composite structures
Session 5 · Data structuresIntervals, bit sets and range-query structures
Session 6 · Algorithms & Big OBig O from first principles
Session 7 · Algorithms & Big OBinary search, two pointers and sliding windows
Session 8 · Algorithms & Big OGraph traversal, dependency ordering and shortest paths
Session 9 · Algorithms & Big ODynamic programming, greedy proofs and backtracking
Session 10 · Algorithms & Big OString matching, hashing and bit manipulation
Session 11 · JavaJava values, references and object contracts
Session 12 · JavaCollections, generics and stream pipelines
Session 13 · JavaVisibility, atomicity and concurrent work
Session 14 · JavaJVM memory, resources and diagnosis
Session 15 · JavaRecords, sealed types and pattern matching
Session 16 · Spring BootSpring container, injection and auto-configuration
Session 17 · Spring BootHTTP boundaries, validation and security
Session 18 · Spring BootTransactions, JPA queries and concurrent updates
Session 19 · Spring BootTesting, resilience and production operation
Session 20 · Spring BootEvents, messaging and idempotent consumers
Session 21 · Object-oriented designObjects, contracts and invariants
Session 22 · Object-oriented designComposition, SOLID and patterns with purpose
Session 23 · Object-oriented designDesigning caches, schedulers and rate limiters
Session 24 · Object-oriented designA complete design round: booking and payment
Session 25 · Object-oriented designDesigning for change: a notification service

Big O quick reference

Define n, separate time from space, and name worst-case, expected or amortized assumptions. Output storage and recursion count too. Database and network latency require an additional cost model.

Complexity growth and assumptions
Growth	Example	Meaning
O(1)	Direct array index	Does not grow with element count; not a latency guarantee.
O(log n)	Binary search	Repeatedly halves a sorted random-access candidate region.
O(n)	Complete scan	Each input item is examined a bounded number of times.
O(n log n)	Merge sort	Logarithmic levels with linear work per level.
O(n²)	Every unordered pair	n(n−1)/2 comparisons still grows quadratically.
O(n · 2^n)	Materialize all subsets	Counts the elements copied into the exponential output.
O(V + E)	Graph traversal	Assumes adjacency lists; a matrix scan is O(V²).

Read the full complexity chapter

Examples use Java 21-era language and Spring Boot 3.5 concepts unless noted. JDK 24 virtual-thread changes and Boot release differences are called out explicitly. These are authored study materials, not claims of recently asked company questions. Each track links its reference documentation.