This is a plain theory question — it asks you to explain the process of Linear Search using the given data.

Linear Search — Step-by-Step Walkthrough

Linear search checks each element of the list one by one, starting from index 0, until the key is found or the list ends.

Given numList = [8, -4, 7, 17, 0, 2, 19] and key = 17:

1. Compare with index 0 (value 8): 8 ≠ 17 → move to next.
2. Compare with index 1 (value -4): -4 ≠ 17 → move to next.
3. Compare with index 2 (value 7): 7 ≠ 17 → move to next.
4. Compare with index 3 (value 17): 17 = 17 → match found at index 3.

The search stops here. The algorithm returns the index 3 (or True / the value itself, depending on implementation).

Time Complexity

• Best case: O(1) — key is at index 0.
• Worst case: O(n) — key is at the last index or not present.
• Average case: O(n).

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This is a plain theory question that asks you to explain the concept of Linear Search using the given example. The core idea is straightforward: when you have an unsorted list and need to find a specific value, you have no shortcuts — you must look at every element in order until you either find what you're looking for or run out of elements.

Why Linear Search is the right tool here. The list [8, -4, 7, 17, 0, 2, 19] is not sorted. If it were sorted, you could use Binary Search, which is much faster. But since the values are in arbitrary order, the only reliable method is to start at index 0 and move forward, comparing each element to the key (17). This is exactly what Linear Search does — it trades speed for simplicity and works on any list.

How the search proceeds for key = 17:

• Start at index 0: value is 8. Not equal to 17. Move to next.
• Index 1: value is -4. Not equal. Move on.
• Index 2: value is 7. Not equal. Move on.
• Index 3: value is 17. Match found. The search stops here.

The algorithm returns the index where the key was found, which is 3. If the key were not present (say we searched for 100), the search would go through all seven elements and then return a failure indicator, typically -1 or None. …