Autocomplete System with Trie in DSA Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a word into the Trie.

DSA Python

def insert(self, word):
    node = self.root
    for char in word:
        if char not in node.children:
            node.children[char] = [1]()
        node = node.children[char]
    node.is_end_of_word = True

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

ATrieNode

Bdict

Clist

Dset

Attempts:

3 left

💡 Hint

Common Mistakes

Using dict or list instead of TrieNode for children.

We create a new TrieNode object for each new character path.

2fill in blank

medium

Complete the code to check if a prefix exists in the Trie.

DSA Python

def starts_with(self, prefix):
    node = self.root
    for char in prefix:
        if char not in node.children:
            return [1]
        node = node.children[char]
    return True

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

A0

BTrue

CNone

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Returning True or None when prefix is missing.

If any character is missing, the prefix does not exist, so return False.

3fill in blank

hard

Fix the error in the search method to correctly find a word in the Trie.

DSA Python

def search(self, word):
    node = self.root
    for char in word:
        if char not in node.children:
            return False
        node = node.children[[1]]
    return node.is_end_of_word

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

Aword

Bchar

Cself

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' or 'self' instead of 'char'.

We must use the current character 'char' to move to the next node.

4fill in blank

hard

Fill both blanks to collect all words starting with a prefix using DFS.

DSA Python

def dfs(self, node, prefix, results):
    if node.is_end_of_word:
        results.append(prefix)
    for [1], child_node in node.children.items():
        self.dfs(child_node, prefix + [2], results)

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

Achar

Bnode

Cprefix

Dchild_node

Attempts:

3 left

💡 Hint

Common Mistakes

Using node or child_node as the character key.

We use 'char' as the key and add it to prefix to explore deeper.

5fill in blank

hard

Fill all three blanks to implement the autocomplete method returning all words with given prefix.

DSA Python

def autocomplete(self, prefix):
    node = self.root
    for [1] in prefix:
        if [2] not in node.children:
            return []
        node = node.children[[3]]
    results = []
    self.dfs(node, prefix, results)
    return results

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

Achar

Bprefix

Dnode

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'prefix' as loop variable or key instead of 'char'.

Use 'char' to iterate and check children keys to find the node for prefix.

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