C Language Searching & Sorting - Practice Questions 2026

Core Concepts: Here, we dive into standard algorithms like Linear Search and Bubble Sort. The focus is on understanding the "why" behind the swaps and comparisons.

Intermediate Concepts: This module covers more efficient algorithms such as Binary Search, Insertion Sort, and Selection Sort. You will explore how to reduce time complexity from $O(n^2)$ to $O(\log n)$ in specific contexts.

Advanced Concepts: Challenge yourself with divide-and-conquer strategies. This includes Merge Sort and Quick Sort, focusing on recursion, partitioning logic, and worst-case performance scenarios.

Real-world Scenarios: Move beyond simple integers. These questions involve sorting structures, handling duplicate keys, and choosing the right algorithm based on specific constraints like limited memory.

Mixed Revision / Final Test: A comprehensive simulation featuring a random mix of all topics. This final hurdle ensures you can identify the correct algorithmic approach without being prompted by a category name.

Option 2: 512

Option 3: 11

Option 4: 10

Option 5: 1

Correct Answer: Option 3

Correct Answer Explanation: Binary Search operates on a logarithmic scale. The formula for the maximum number of comparisons is $\lceil \log_2(n) \rceil + 1$. For 1024 elements ($2^{10}$), the search takes 10 divisions to reach a single element, plus one final comparison to confirm the value. Thus, 11 is the safest upper bound for a worst-case scenario.

Wrong Answers Explanation:

Option 2: This is a common misconception that searching half the array takes $n/2$ comparisons.

Option 4: This is exactly $\log_2(1024)$, but it often misses the final check comparison in practical implementations.

Option 5: This is the best-case scenario (finding the element at the first midpoint).

Option 2: Bubble Sort

Option 3: Merge Sort

Option 4: Quick Sort

Option 5: None of the above

Correct Answer: Option 4

Correct Answer Explanation: Quick Sort is generally unstable because the partitioning process can swap non-adjacent elements, potentially reordering two elements with equal keys. While it can be made stable with extra overhead, its standard in-place version is not.

Wrong Answers Explanation:

Option 2: Bubble Sort is stable because it only swaps adjacent elements if they are out of order.

Option 3: Merge Sort is inherently stable as long as the merge logic favors the left sub-array for equal values.

Option 5: This is incorrect as a well-known unstable algorithm is present in the list.

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