Comparison Based vs Non Comparison Based Sorting in DSA C# - Interactive Comparison Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a comparison-based sort using Array.Sort.

DSA C#

int[] numbers = {5, 3, 8, 1};
Array.[1](numbers);
foreach (int num in numbers) {
    Console.Write(num + " ");
}

Drag options to blanks, or click blank then click option'

AOrder

BOrderBy

CSortBy

DSort

Attempts:

3 left

💡 Hint

Common Mistakes

Using LINQ methods like OrderBy which return new sequences instead of sorting in place.

Using incorrect method names like SortBy or Order.

Array.Sort is the built-in method in C# for comparison-based sorting of arrays.

2fill in blank

medium

Complete the code to perform a non-comparison based sort using Counting Sort logic for integers.

DSA C#

int[] arr = {4, 2, 2, 8, 3, 3, 1};
int max = arr.Max();
int[] count = new int[max + 1];
foreach (int num in arr) {
    count[[1]]++;
}

Drag options to blanks, or click blank then click option'

Amax

Barr

Cnum

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using the array name or max as index instead of the current number.

Incrementing the wrong array.

In Counting Sort, we increment the count at the index equal to the number itself.

3fill in blank

hard

Fix the error in the code to correctly print the sorted array after Counting Sort.

DSA C#

int[] count = {0, 1, 2, 1};
for (int i = 0; i < count.Length; i++) {
    for (int j = 0; j < count[[1]]; j++) {
        Console.Write(i + " ");
    }
}

Drag options to blanks, or click blank then click option'

Ai

Bj

Ccount

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'j' as index which is the inner loop counter.

Using 'count' or '0' as index which causes errors.

The inner loop should run count[i] times to print the number i that many times.

4fill in blank

hard

Fill both blanks to complete the code that builds a dictionary comprehension filtering numbers greater than 3.

DSA C#

var result = new Dictionary<int, int>();
foreach (var num in numbers) {
    if (num [1] 3) {
        result[num] = num [2] 1;
    }
}

Drag options to blanks, or click blank then click option'

A>

B+

C<

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than instead of greater than in the condition.

Subtracting 1 instead of adding 1.

We check if num is greater than 3 and then add 1 to num for the dictionary value.

5fill in blank

hard

Fill all three blanks to complete the LINQ expression that selects keys in uppercase where values are positive.

DSA C#

var filtered = data.Where(kv => kv.Value [1] 0)
                   .ToDictionary(kv => kv.Key.[2](), kv => kv.[3]);

Drag options to blanks, or click blank then click option'

A>

BToUpper

CValue

DToLower

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > in the filter.

Using ToLower instead of ToUpper for keys.

Using Key instead of Value for the dictionary values.

We filter values greater than 0, convert keys to uppercase, and keep values as is.

Practice

1. Which of the following sorting algorithms is non-comparison based?

easy

A. Merge Sort

B. Quick Sort

C. Counting Sort

D. Heap Sort

Solution

1. Step 1: Understand comparison based sorting

   Comparison based sorting algorithms decide order by comparing elements, like Merge Sort, Quick Sort, and Heap Sort.

2. Step 2: Identify non-comparison based sorting

   Counting Sort uses counting frequencies and does not compare elements directly.

3. Final Answer:

   Counting Sort -> Option C

4. Quick Check:

   Non-comparison based = Counting Sort [OK]

Hint: Non-comparison sorts use counting or buckets, not element comparisons [OK]

Common Mistakes:

• Confusing Quick Sort as non-comparison
• Thinking Heap Sort is non-comparison
• Assuming all sorts compare elements

2. Which of the following is the correct syntax to call Radix Sort method named RadixSort on an integer array arr in C#?

easy

A. RadixSort(arr);

B. arr.RadixSort();

C. RadixSort<int>(arr);

D. RadixSort arr;

Solution

1. Step 1: Understand method call syntax in C#

   Calling a static method with an array argument uses method name and parentheses with the array inside.

2. Step 2: Analyze options

   RadixSort(arr); uses correct syntax: RadixSort(arr); arr.RadixSort(); tries to call as instance method which is incorrect unless defined so. RadixSort<int>(arr); uses generic syntax unnecessarily. RadixSort arr; is invalid syntax.

3. Final Answer:

   RadixSort(arr); -> Option A

4. Quick Check:

   Static method call = RadixSort(arr); [OK]

Hint: Static method calls use MethodName(args) syntax [OK]

Common Mistakes:

• Using instance method call on array
• Adding unnecessary generic syntax
• Writing invalid declaration instead of call

3. What will be the output of the following C# code snippet?

int[] arr = {4, 2, 2, 8, 3, 3, 1};
CountingSort(arr);
Console.WriteLine(string.Join(", ", arr));

void CountingSort(int[] array) {
    int max = array.Max();
    int[] count = new int[max + 1];
    foreach (int num in array) count[num]++;
    int index = 0;
    for (int i = 0; i <= max; i++) {
        while (count[i] > 0) {
            array[index++] = i;
            count[i]--;
        }
    }
}

medium

A. 8, 4, 3, 3, 2, 2, 1

B. 1, 2, 2, 3, 3, 4, 8

C. 4, 2, 2, 8, 3, 3, 1

D. 1, 1, 2, 2, 3, 3, 4

Solution

1. Step 1: Understand Counting Sort logic

   Counting Sort counts occurrences of each number and then writes them back in sorted order.

2. Step 2: Trace the code with input array

   Input: {4, 2, 2, 8, 3, 3, 1} Count array: counts of 1=1, 2=2, 3=2, 4=1, 8=1 Output array after sorting: 1, 2, 2, 3, 3, 4, 8

3. Final Answer:

   1, 2, 2, 3, 3, 4, 8 -> Option B

4. Quick Check:

   Counting Sort output = sorted array [OK]

Hint: Counting Sort outputs sorted array by frequency count [OK]

Common Mistakes:

• Assuming original array remains unchanged
• Confusing Counting Sort with reverse order
• Miscounting frequencies

4. The following code attempts to implement Radix Sort but has a bug. What is the error?

void RadixSort(int[] arr) {
    int max = arr.Max();
    for (int exp = 1; max / exp > 0; exp *= 10) {
        CountingSortByDigit(arr, exp);
    }
}

void CountingSortByDigit(int[] arr, int exp) {
    int n = arr.Length;
    int[] output = new int[n];
    int[] count = new int[10];

    for (int i = 0; i < n; i++)
        count[(arr[i] / exp) % 10]++;

    for (int i = 1; i < 10; i++)
        count[i] += count[i - 1];

    for (int i = n - 1; i >= 0; i--) {
        output[count[(arr[i] / exp) % 10] - 1] = arr[i];
        count[(arr[i] / exp) % 10]--;
    }

    // Missing step here
}

medium

A. The loop for counting prefix sums should start from 0

B. The count array size should be 100 instead of 10

C. The exponent variable exp should be incremented by 1 each iteration

D. The sorted output array is not copied back to the original array

Solution

1. Step 1: Analyze CountingSortByDigit function

   The function builds output array sorted by digit but does not copy output back to arr.

2. Step 2: Identify missing step

   After building output, we must copy output elements back to arr to update it for next digit sorting.

3. Final Answer:

   The sorted output array is not copied back to the original array -> Option D

4. Quick Check:

   Missing copy back step = bug [OK]

Hint: Radix Sort needs copying output back each digit pass [OK]

Common Mistakes:

• Thinking count array size should be larger
• Starting prefix sum loop incorrectly
• Incrementing exp by 1 instead of multiplying by 10

5. You have an array of 1 million integers ranging from 0 to 1000. Which sorting algorithm is the most efficient choice and why?

hard

A. Counting Sort, because it sorts in O(n + k) time where k is max value

B. Heap Sort, because it uses constant extra space

C. Merge Sort, because it is stable and works well on large data

D. Quick Sort, because it has average O(n log n) time

Solution

1. Step 1: Analyze input size and range

   Array size n = 1,000,000; max value k = 1000 (small compared to n).

2. Step 2: Compare algorithm complexities

   Counting Sort runs in O(n + k) which is O(1,001,000) here, faster than O(n log n) for comparison sorts.

3. Step 3: Choose best algorithm

   Counting Sort is efficient for small range large data, so best choice.

4. Final Answer:

   Counting Sort, because it sorts in O(n + k) time where k is max value -> Option A

5. Quick Check:

   Small range + large n = Counting Sort best [OK]

Hint: Use Counting Sort for large arrays with small value range [OK]

Common Mistakes:

• Choosing Quick Sort ignoring small range advantage
• Preferring Merge Sort for speed over Counting Sort
• Ignoring Counting Sort's linear time benefit