Autocomplete System with Trie in DSA Python - Practice Problems & Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Trie Insertion and Search

What is the output of the following code that inserts words into a Trie and searches for a prefix?

DSA Python

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.is_end = True

    def starts_with(self, prefix):
        node = self.root
        for char in prefix:
            if char not in node.children:
                return False
            node = node.children[char]
        return True

trie = Trie()
words = ["dog", "deer", "deal"]
for w in words:
    trie.insert(w)

print(trie.starts_with("de"))
print(trie.starts_with("da"))

A True\nTrue

B False\nTrue

C False\nFalse

D True\nFalse

💡 Hint

Check if the prefix exists in the Trie nodes after insertion.

The words 'deer' and 'deal' start with 'de', so starts_with('de') returns True. No word starts with 'da', so starts_with('da') returns False.

🧠 Conceptual

intermediate

Understanding Trie Node Structure

Which of the following best describes the structure of a Trie node used in an autocomplete system?

A A node contains a pointer to the next node and a count of words passing through it.

B A node contains a dictionary of children nodes and a boolean indicating if it is the end of a word.

C A node contains only a list of words that start with the prefix up to that node.

D A node contains a single character and a pointer to the parent node.

💡 Hint

Think about how to efficiently check prefixes and word endings.

Each Trie node holds children nodes in a dictionary keyed by characters and a boolean to mark if a word ends there, enabling prefix and word searches.

🔧 Debug

advanced

Identify the Bug in Autocomplete Suggestion Code

What error will the following code produce when trying to get autocomplete suggestions for prefix 'ca'?

DSA Python

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.is_end = True

    def _dfs(self, node, prefix, results):
        if node.is_end:
            results.append(prefix)
        for char in node.children:
            self._dfs(node.children[char], prefix + char, results)

    def autocomplete(self, prefix):
        node = self.root
        for char in prefix:
            if char not in node.children:
                return []
            node = node.children[char]
        results = []
        self._dfs(node, prefix, results)
        return results

trie = Trie()
words = ["cat", "car", "cart", "dog"]
for w in words:
    trie.insert(w)

print(trie.autocomplete("ca"))

A TypeError: 'int' object is not iterable

B AttributeError: 'NoneType' object has no attribute 'children'

C No error; output: ['cat', 'car', 'cart']

D IndexError: string index out of range

💡 Hint

Check if the DFS method correctly traverses children nodes.

The code correctly inserts words and performs DFS to collect all words starting with 'ca'. No error occurs and output is ['cat', 'car', 'cart'].

🚀 Application

advanced

Number of Words with Given Prefix

Given a Trie with inserted words ['apple', 'app', 'application', 'bat', 'ball'], what is the number of words that start with prefix 'app'?

A 3

B 2

C 4

D 1

💡 Hint

Count all words that start with 'app' including the prefix itself if it is a word.

'apple', 'app', and 'application' all start with 'app', so the count is 3.

🧠 Conceptual

expert

Why Use Trie for Autocomplete Instead of Hash Map?

Why is a Trie data structure preferred over a hash map for implementing an autocomplete system?

A Trie allows efficient prefix search and retrieval of all words with a given prefix, while hash maps do not support prefix queries efficiently.

B Trie uses less memory than hash maps for storing words.

C Hash maps cannot store strings as keys.

D Trie automatically sorts words alphabetically, hash maps do not.

💡 Hint

Consider how prefix queries are handled in both data structures.

Tries are designed to share common prefixes and allow fast prefix-based searches, which hash maps cannot do efficiently without scanning all keys.

Practice

1. What is the main purpose of using a Trie in an autocomplete system?

easy

A. To sort words alphabetically

B. To efficiently store and search prefixes of words

C. To count the total number of words

D. To encrypt the stored words

Solution

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

   A Trie is a tree-like data structure designed to store strings by their prefixes, allowing quick prefix-based searches.

2. Step 2: Connect Trie usage to autocomplete

   Autocomplete requires finding all words starting with a given prefix efficiently, which Trie supports by traversing nodes matching the prefix.

3. Final Answer:

   To efficiently store and search prefixes of words -> Option B

4. Quick Check:

   Trie = prefix search [OK]

Hint: Trie stores prefixes for fast autocomplete search [OK]

Common Mistakes:

• Thinking Trie sorts words alphabetically
• Confusing Trie with hash tables
• Assuming Trie encrypts data

2. Which of the following is the correct way to insert a word "cat" into a Trie node in Python?

easy

A. node['c'] = TrieNode()

B. node.insert('c')

C. node.add('c')

D. node.children['c'] = TrieNode()

Solution

1. Step 1: Understand Trie node structure

   Trie nodes typically have a dictionary called children to map characters to child nodes.

2. Step 2: Insert character as a key in children dictionary

   To insert 'c', assign a new TrieNode to node.children['c'].

3. Final Answer:

   node.children['c'] = TrieNode() -> Option D

4. Quick Check:

   Insert char in children dict = node.children['c'] = TrieNode() [OK]

Hint: Use children dictionary to add new character nodes [OK]

Common Mistakes:

• Trying to assign directly to node['c'] without children
• Using non-existent methods like add or insert
• Confusing list with dictionary for children

3. Given the following code snippet, what will be the output?

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.is_end = True

    def search_prefix(self, prefix):
        node = self.root
        for char in prefix:
            if char not in node.children:
                return False
            node = node.children[char]
        return True

trie = Trie()
trie.insert('dog')
trie.insert('deer')
print(trie.search_prefix('de'))
print(trie.search_prefix('do'))
print(trie.search_prefix('da'))

medium

A. True\nFalse\nFalse

B. False\nTrue\nFalse

C. True\nTrue\nFalse

D. False\nFalse\nFalse

Solution

1. Step 1: Insert words 'dog' and 'deer' into Trie

   Both words add nodes for their characters. 'dog' adds d->o->g, 'deer' adds d->e->e->r.

2. Step 2: Check prefixes 'de', 'do', and 'da'

   'de' exists as prefix in 'deer', so returns True. 'do' exists as prefix in 'dog', so True. 'da' does not exist, so False.

3. Final Answer:

   True True False -> Option C

4. Quick Check:

   Prefix 'de' and 'do' found, 'da' not found = True, True, False [OK]

Hint: Check prefix path existence in Trie children [OK]

Common Mistakes:

• Confusing search_prefix with full word search
• Assuming 'da' prefix exists
• Ignoring is_end flag for prefix search

4. The following code is intended to insert words into a Trie, but it raises an error. What is the error?

class TrieNode:
    def __init__(self):
        self.children = []
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]

medium

A. TypeError: list indices must be integers or slices, not str

B. AttributeError: 'TrieNode' object has no attribute 'children'

C. SyntaxError: invalid syntax

D. No error, code runs fine

Solution

1. Step 1: Identify data structure used for children

   The code uses a list for children, but tries to access it with string keys.

2. Step 2: Understand list indexing rules

   Lists require integer indices, but code uses node.children[char] where char is a string, causing TypeError.

3. Final Answer:

   TypeError: list indices must be integers or slices, not str -> Option A

4. Quick Check:

   Using list with string keys causes TypeError [OK]

Hint: Use dictionary, not list, for children to index by characters [OK]

Common Mistakes:

• Using list instead of dict for children
• Ignoring error message details
• Assuming code runs without error

5. You want to implement an autocomplete system that returns all words starting with a given prefix using a Trie. Which approach correctly collects all matching words after reaching the prefix node?

hard

A. Perform a depth-first search (DFS) from the prefix node, collecting words where is_end is True

B. Return all children keys of the prefix node without further traversal

C. Only return the prefix itself without searching deeper

D. Use breadth-first search (BFS) but ignore is_end flags

Solution

1. Step 1: Reach the node matching the prefix

   Traverse the Trie to the node representing the last character of the prefix.

2. Step 2: Collect all words starting from prefix node using DFS

   Use DFS to explore all child nodes recursively, collecting words where is_end is True to get complete words.

3. Final Answer:

   Perform a depth-first search (DFS) from the prefix node, collecting words where is_end is True -> Option A

4. Quick Check:

   DFS from prefix node with is_end check = correct autocomplete [OK]

Hint: Use DFS from prefix node, collect words at is_end True [OK]

Common Mistakes:

• Returning only immediate children keys
• Ignoring is_end flags and returning incomplete words
• Using BFS without checking word ends