Autocomplete System with Trie in DSA C# - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a character into the Trie node's children dictionary.

DSA C#

node.Children[[1]] = new TrieNode();

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

Aword[i]

Bword

Ci

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole word instead of a single character.

Using the index i instead of the character at that index.

We insert the character at index i of the word as the key in the children dictionary.

2fill in blank

medium

Complete the code to check if the Trie node has a child for the current character.

DSA C#

if (!node.Children.ContainsKey([1])) { return new List<string>(); }

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Aword[i]

Bword

Cnode

Di

Attempts:

3 left

💡 Hint

Common Mistakes

Checking for the whole word instead of a character.

Using the node variable instead of a character.

We check if the children dictionary contains the character at index i of the word.

3fill in blank

hard

Fix the error in the recursive function to collect all words from the Trie node.

DSA C#

foreach (var child in node.Children) { CollectWords(child.Value, prefix + child.Key, [1]); }

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Achild

Bprefix

Cnode

Dresults

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the prefix string instead of the results list.

Passing the node or child instead of the results list.

We pass the results list to collect words recursively.

4fill in blank

hard

Fill both blanks to complete the method that returns autocomplete suggestions for a prefix.

DSA C#

TrieNode node = root;
for (int i = 0; i < prefix.Length; i++) {
    if (!node.Children.ContainsKey([1])) return new List<string>();
    node = node.Children[[2]];
}
return CollectWords(node, prefix, new List<string>());

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

Aprefix[i]

Bprefix

Ci

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole prefix instead of a single character.

Using the index i or node instead of the character.

We use the character at index i of prefix to check and move to the child node.

5fill in blank

hard

Fill all three blanks to complete the TrieNode class constructor and the Insert method.

DSA C#

public class TrieNode {
    public Dictionary<char, TrieNode> Children = new Dictionary<char, TrieNode>();
    public bool IsWordEnd = false;

    public TrieNode() {
        Children = new Dictionary<char, TrieNode>();
        IsWordEnd = [1];
    }
}

public void Insert(string word) {
    TrieNode node = root;
    foreach (char c in word) {
        if (!node.Children.ContainsKey(c)) {
            node.Children[c] = new TrieNode();
        }
        node = node.Children[c];
    }
    node.IsWordEnd = [2];
}

public TrieNode root = new TrieNode[3];

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Afalse

Btrue

C()

Attempts:

3 left

💡 Hint

Common Mistakes

Setting IsWordEnd to true in constructor.

Not marking the end of word as true in Insert.

Not calling the constructor when creating root.

The constructor sets IsWordEnd to false initially. Insert marks the end of a word as true. The root is created with an empty constructor call.

Practice

1. What is the main advantage of using a Trie over a HashSet for implementing an autocomplete system?

easy

A. Trie uses less memory than HashSet for all cases

B. Trie supports prefix-based search efficiently, while HashSet requires checking all words

C. Trie is faster for exact word lookup than HashSet

D. Trie automatically sorts words alphabetically unlike HashSet

Solution

1. Step 1: Understand autocomplete requirement

   Autocomplete needs to find all words that start with a given prefix quickly.

2. Step 2: Compare Trie vs HashSet

   Trie stores characters level by level, so prefix traversal is O(prefix length). HashSet requires scanning all words to check which ones start with the prefix.

3. Final Answer:

   Trie supports prefix-based search efficiently, while HashSet requires checking all words -> Option B

4. Quick Check:

   Trie = prefix search in O(m), HashSet = O(n*m) for prefix [OK]

Hint: Trie shares common prefixes among words - natural fit for autocomplete [OK]

Common Mistakes:

• Assuming Trie always uses less memory - it can use more for sparse word sets
• Thinking exact lookup is why Trie beats HashSet - HashSet is actually faster for exact lookup
• Assuming Trie sorts automatically - it doesn't sort, traversal order depends on children insertion

2. Which C# code correctly inserts a word into a Trie node structure where each node has a Dictionary<char, TrieNode> Children and a bool IsEndOfWord?

easy

A. node.Children[c] = new TrieNode(); node = node.Children[c];

B. if (!node.Children.ContainsKey(c)) node.Children[c] = new TrieNode(); node = node.Children[c];

C. node.Children.Add(c); node = node.Children[c];

D. node.Children[c] = c; node = node.Children[c];

Solution

1. Step 1: Check if child exists

   Before creating a new node, check if the character key already exists in Children to avoid overwriting existing subtrees.

2. Step 2: Create only if missing

   Using ContainsKey check ensures we reuse existing nodes and only create new ones when the character path doesn't exist yet.

3. Final Answer:

   if (!node.Children.ContainsKey(c)) node.Children[c] = new TrieNode(); node = node.Children[c]; -> Option B

4. Quick Check:

   Always check ContainsKey before adding to avoid overwriting existing paths [OK]

Hint: Always guard with ContainsKey before inserting a new Trie node [OK]

Common Mistakes:

• Option A overwrites existing children, destroying previously inserted words
• Option C uses Add which throws if key already exists
• Option D stores char as value instead of TrieNode

3. What will the following C# Trie prefix search return when searching for prefix "ca" in a Trie containing ["cat", "car", "card", "dog"]?

List<string> results = new List<string>();
void DFS(TrieNode node, string current) {
    if (node.IsEndOfWord) results.Add(current);
    foreach (var kvp in node.Children)
        DFS(kvp.Value, current + kvp.Key);
}
TrieNode prefixNode = GetPrefixNode("ca");
if (prefixNode != null) DFS(prefixNode, "ca");

medium

A. ["cat", "car", "card", "dog"]

B. ["ca"]

C. ["cat", "car", "card"]

D. []

Solution

1. Step 1: Find prefix node

   GetPrefixNode("ca") traverses c→a and returns the node at 'a'. "dog" is unreachable from this node.

2. Step 2: DFS from prefix node

   DFS explores all children from 'a': t (cat), r (car), r→d (card). All three have IsEndOfWord=true.

3. Final Answer:

   ["cat", "car", "card"] -> Option C

4. Quick Check:

   Only words under prefix "ca" subtree are returned, "dog" excluded [OK]

Hint: DFS from prefix node returns only words in that subtree - "dog" is in a different branch [OK]

Common Mistakes:

• Including 'dog' - it's in a completely separate Trie branch
• Returning only 'ca' - 'ca' itself is not marked IsEndOfWord
• Returning empty - prefix 'ca' exists in the Trie so DFS finds words

4. Identify the bug in this C# autocomplete prefix search:

bool StartsWith(string prefix) {
    TrieNode node = root;
    foreach (char c in prefix) {
        if (!node.Children.ContainsKey(c))
            return true;
        node = node.Children[c];
    }
    return false;
}

medium

A. Should return false when character not found, and true at the end

B. Should use a List instead of Dictionary for Children

C. Should check IsEndOfWord instead of ContainsKey

D. Should start from the last character of the prefix

Solution

1. Step 1: Trace the logic

   When a character is not found in Children, it means the prefix doesn't exist - should return false, not true. And after traversing all prefix characters successfully, the prefix exists - should return true.

2. Step 2: Identify the swap

   The return values are swapped: 'return true' and 'return false' are in each other's place.

3. Final Answer:

   Should return false when character not found, and true at the end -> Option A

4. Quick Check:

   Missing prefix char = false, full prefix traversed = true [OK]

Hint: If you can't follow the path, prefix is absent = false; if path complete = true [OK]

Common Mistakes:

• Thinking Dictionary is wrong - Dictionary is correct for Children
• Checking IsEndOfWord - StartsWith only checks if prefix path exists, not word end
• Starting from last character - prefix traversal always goes left to right

5. In a Trie-based autocomplete system with 100,000 words averaging 8 characters each, what is the time complexity to retrieve all words matching a prefix of length p, where k is the number of matching words and m is the average word length?

hard

A. O(n) where n is total number of words

B. O(p + k * m) - traverse prefix then DFS over matching words

C. O(p * n) - check prefix against each word

D. O(1) - Trie provides constant time autocomplete

Solution

1. Step 1: Cost of reaching prefix node

   Traversing p characters to reach the prefix node costs O(p).

2. Step 2: Cost of DFS to collect results

   DFS visits all nodes under the prefix subtree. With k matching words of average length m, this costs O(k * m) in the worst case.

3. Final Answer:

   O(p + k * m) -> Option B

4. Quick Check:

   Prefix traversal O(p) + result collection O(k*m) = O(p + k*m) [OK]

Hint: Trie autocomplete = prefix path O(p) + collect results O(k*m), independent of total word count n [OK]

Common Mistakes:

• O(n) - Trie doesn't scan all words, only the prefix subtree
• O(1) - result collection depends on number and length of matching words
• O(p*n) - that's the HashSet approach, not Trie