Algorithms: sorting, searching, and Big-O

What this quiz covers

From bubble vs. quicksort to binary search and asymptotic notation, this quiz targets the core ideas behind algorithm performance and correctness.

Each question uses 4 options and there’s no timer, so you can slow down, do the math, and build reliable intuition instead of rushing.

Skills you’ll practice

You’ll practice reading pseudocode-like descriptions, estimating runtime, and choosing the right approach for a given constraint (sorted input, memory limits, duplicates, worst-case behavior).

Common pitfalls include mixing up average vs. worst case, assuming binary search works on unsorted data, and treating Big-O as an exact runtime rather than a growth rate.

Difficulty and how to play

Difficulty is mixed on purpose: easier items reinforce definitions and standard complexities, while harder ones push you to compare trade-offs and reason about edge cases. Choose your question count and difficulty before starting to tailor the session - use shorter runs for quick review or longer runs to build stamina.

• Identify the Big-O (and sometimes Big-Theta) of common loops and recurrences
• Compare sorting algorithms by stability, in-place behavior, and typical performance
• Decide when linear search beats binary search in real constraints
• Spot off-by-one and boundary issues in search logic
• Interpret best/average/worst-case scenarios without conflating them
• Connect problem constraints to an algorithm choice (time vs. space trade-offs)

Tips to score higher

Write down assumptions (sortedness, duplicates, input size) before answering, and sanity-check with small examples. When stuck, eliminate options by growth rate: constants < log n < n < n log n < n² < 2ⁿ.