Prefix Search Using Trie in DSA C - Practice Problems & Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Prefix Search Result

What is the output of the prefix search for "app" after inserting the words "apple", "app", "application", and "apt" into the trie?

DSA C

/* Insert words into trie: apple, app, application, apt */
/* Then search for prefix "app" and list all words with this prefix */

Output format: JSON array of strings sorted lexicographically.

A ["app", "apple", "application"]

B ["app", "apt"]

C ["apple", "application"]

D ["app", "apple", "application", "apt"]

💡 Hint

Remember that only words starting exactly with the prefix "app" should be included.

The prefix "app" matches words starting with "app". "apt" starts with "ap" but not "app", so it is excluded.

🧠 Conceptual

intermediate

Number of Nodes in Trie After Insertions

After inserting the words "cat", "car", "cart", and "dog" into an empty trie, how many nodes does the trie contain?

A 8

B 10

C 7

D 9

💡 Hint

Count shared prefixes carefully to avoid double counting nodes.

The trie nodes are: root + c + a + t + r + d + o + g. The path for "car" and "cart" shares nodes c->a->r, so total nodes are 9.

🔧 Debug

advanced

Identify the Bug in Trie Insertion

What error will occur when running this trie insertion code snippet? ```c void insert(struct TrieNode* root, const char* word) { struct TrieNode* node = root; for (int i = 0; word[i] != '\0'; i++) { int index = word[i] - 'a'; if (!node->children[index]) node->children[index] = malloc(sizeof(struct TrieNode)); node = node->children[index]; } node->isEndOfWord = 1; } ``` Assume struct TrieNode has a children array of 26 pointers and an int isEndOfWord.

DSA C

void insert(struct TrieNode* root, const char* word) {
    struct TrieNode* node = root;
    for (int i = 0; word[i] != '\0'; i++) {
        int index = word[i] - 'a';
        if (!node->children[index])
            node->children[index] = malloc(sizeof(struct TrieNode));
        node = node->children[index];
    }
    node->isEndOfWord = 1;
}

A Segmentation fault due to uninitialized children pointers in new nodes

B No error, code runs correctly

C Memory leak due to missing free calls

D Compilation error due to missing struct definition

💡 Hint

Check what happens to the children pointers of newly allocated nodes.

New nodes are allocated but their children pointers are not initialized to NULL, so accessing them later causes segmentation fault.

📝 Syntax

advanced

Correct Syntax for Trie Node Initialization

Which option correctly initializes a new trie node in C with all children pointers set to NULL and isEndOfWord set to 0?

A struct TrieNode* node = malloc(sizeof(struct TrieNode)); memset(node->children, 0, sizeof(node->children)); node->isEndOfWord = 0;

B struct TrieNode* node = calloc(1, sizeof(struct TrieNode));

C struct TrieNode node; for (int i=0; i<26; i++) node.children[i] = NULL; node.isEndOfWord = 0;

D struct TrieNode* node = malloc(sizeof(struct TrieNode)); node->children = NULL; node->isEndOfWord = 0;

💡 Hint

calloc initializes allocated memory to zero.

Option B uses calloc which sets all bytes to zero, so children pointers become NULL and isEndOfWord is 0.

🚀 Application

expert

Longest Common Prefix Using Trie

Given the words ["flower", "flow", "flight"], what is the longest common prefix found using a trie?

A "f"

B "flo"

C "fl"

D "flow"

💡 Hint

The longest common prefix is the longest path from root where all words share the same character.

All words start with 'fl', but 'flow' and 'flower' share 'flo', 'flight' does not, so longest common prefix is "fl".

Practice

1. What is the main purpose of using a Trie data structure in prefix search?

easy

A. To perform arithmetic operations quickly

B. To sort words alphabetically

C. To store numbers in sorted order

D. To efficiently find all words that start with a given prefix

Solution

1. Step 1: Understand the role of Trie in prefix search

   A Trie stores characters of words in a tree structure, allowing quick prefix lookups.

2. Step 2: Identify the main use case

   It helps find all words starting with a prefix efficiently, unlike sorting or arithmetic.

3. Final Answer:

   To efficiently find all words that start with a given prefix -> Option D

4. Quick Check:

   Trie = Prefix search efficiency [OK]

Hint: Trie is built for prefix-based word retrieval [OK]

Common Mistakes:

• Confusing Trie with sorting algorithms
• Thinking Trie is for arithmetic
• Assuming Trie stores numbers only

2. Which of the following is the correct way to declare a Trie node structure in C for prefix search?

easy

A. typedef struct TrieNode { int data; struct TrieNode* next; } TrieNode;

B. typedef struct TrieNode { char* word; struct TrieNode* child; } TrieNode;

C. typedef struct TrieNode { struct TrieNode* children[26]; int isEndOfWord; } TrieNode;

D. typedef struct TrieNode { int value; struct TrieNode* left; struct TrieNode* right; } TrieNode;

Solution

1. Step 1: Identify Trie node components

   A Trie node has an array of pointers for each alphabet letter and a flag for word end.

2. Step 2: Match with given options

   typedef struct TrieNode { struct TrieNode* children[26]; int isEndOfWord; } TrieNode; matches this structure with 26 children pointers and an end-of-word flag.

3. Final Answer:

   typedef struct TrieNode { struct TrieNode* children[26]; int isEndOfWord; } TrieNode; -> Option C

4. Quick Check:

   Trie node = children array + end flag [OK]

Hint: Trie nodes have 26 children pointers for letters a-z [OK]

Common Mistakes:

• Using linked list pointers instead of array for children
• Confusing Trie node with linked list or binary tree node
• Missing end-of-word flag

3. Given the following code snippet inserting words "cat", "car", and "dog" into a Trie, what will be the output of searching prefix "ca"?

insert("cat");
insert("car");
insert("dog");
searchPrefix("ca");

medium

A. ["cat", "car"]

B. ["dog"]

C. ["cat"]

D. []

Solution

1. Step 1: Insert words into Trie

   Words "cat", "car", and "dog" are inserted, building branches for 'c' and 'd'.

2. Step 2: Search prefix "ca"

   Both "cat" and "car" start with "ca", so they are returned by searchPrefix.

3. Final Answer:

   ["cat", "car"] -> Option A

4. Quick Check:

   Prefix "ca" matches cat and car [OK]

Hint: Words starting with prefix are returned [OK]

Common Mistakes:

• Returning only one matching word
• Returning words not matching prefix
• Empty result for existing prefix

4. Consider this buggy C code snippet for inserting a word into a Trie. What is the main error?

void insert(TrieNode* root, char* word) {
  TrieNode* node = root;
  for (int i = 0; word[i] != '\0'; i++) {
    int index = word[i] - 'a';
    if (node->children[index] == NULL) {
      node->children[index] = malloc(sizeof(TrieNode));
    }
    node = node->children[index];
  }
  node->isEndOfWord = 1;
}

medium

A. New nodes are not initialized after malloc

B. Index calculation is incorrect

C. Loop condition should be i < strlen(word)

D. isEndOfWord flag is set too early

Solution

1. Step 1: Analyze node creation

   malloc allocates memory but does not initialize children pointers or isEndOfWord flag.

2. Step 2: Identify impact

   Uninitialized children pointers can cause undefined behavior during traversal or insertion.

3. Final Answer:

   New nodes are not initialized after malloc -> Option A

4. Quick Check:

   malloc needs initialization [OK]

Hint: Always initialize new Trie nodes after malloc [OK]

Common Mistakes:

• Assuming malloc zeroes memory
• Ignoring initialization of isEndOfWord
• Misunderstanding loop condition

5. You want to find all words in a Trie that start with prefix "app" and have length exactly 5. Which approach correctly combines prefix search and length filtering?

hard

A. Collect all words in Trie, then filter those starting with "app" and length 5

B. Traverse Trie to node for "app", then collect words of length 5 from there

C. Search for "app" as a full word, then check length 5

D. Use binary search on sorted word list for prefix "app" and length 5

Solution

1. Step 1: Use Trie prefix traversal

   Traverse the Trie to the node representing the prefix "app" to limit search space.

2. Step 2: Collect words of length 5 from that node

   From this node, recursively collect words that complete to length 5.

3. Final Answer:

   Traverse Trie to node for "app", then collect words of length 5 from there -> Option B

4. Quick Check:

   Prefix traversal + length filter = Traverse Trie to node for "app", then collect words of length 5 from there [OK]

Hint: Start at prefix node, then filter by length [OK]

Common Mistakes:

• Filtering all words instead of prefix subtree
• Searching prefix as full word
• Using binary search on Trie data