The Key PHP Interview Questions that makes the biggest impact in hiring

function processLargeCSV($filename) {
    $handle = fopen($filename, 'r');
    $header = fgetcsv($handle);
    
    while (($row = fgetcsv($handle)) !== false) {
        $record = array_combine($header, $row);
        
        // Transform data
        $transformed = [
            'user_id' => (int)$record['id'],
            'email' => strtolower(trim($record['email'])),
            'created_at' => date('Y-m-d H:i:s', strtotime($record['date']))
        ];
        
        yield $transformed;
        
        // Memory cleanup every 1000 records
        if (ftell($handle) % 1000 === 0) {
            gc_collect_cycles();
        }
    }
    
    fclose($handle);
}

class SchemaEvolution {
    private $schemaVersions = [];
    
    public function registerSchema($version, $schema) {
        $this->schemaVersions[$version] = $schema;
    }
    
    public function transformData($data, $fromVersion, $toVersion) {
        if ($fromVersion === $toVersion) {
            return $data;
        }
        
        $transformations = $this->getTransformationPath($fromVersion, $toVersion);
        
        foreach ($transformations as $transform) {
            $data = $this->applyTransformation($data, $transform);
        }
        
        return $data;
    }
    
    private function applyTransformation($data, $transform) {
        foreach ($transform['mappings'] as $oldField => $newField) {
            if (isset($data[$oldField])) {
                $data[$newField] = $data[$oldField];
                unset($data[$oldField]);
            }
        }
        return $data;
    }
}

class DataAggregator {
    public function groupByAndSum($data, $groupField, $sumField) {
        $result = [];
        
        foreach ($data as $record) {
            $key = $record[$groupField];
            if (!isset($result[$key])) {
                $result[$key] = [
                    $groupField => $key,
                    'sum' => 0,
                    'count' => 0
                ];
            }
            
            $result[$key]['sum'] += $record[$sumField];
            $result[$key]['count']++;
        }
        
        return array_values($result);
    }
    
    public function rollingAverage($data, $window = 3) {
        $result = [];
        $buffer = [];
        
        foreach ($data as $value) {
            $buffer[] = $value;
            if (count($buffer) > $window) {
                array_shift($buffer);
            }
            
            $result[] = array_sum($buffer) / count($buffer);
        }
        
        return $result;
    }
}

class SimpleNeuralNetwork {
    private $weights;
    private $biases;
    private $learningRate;
    
    public function __construct($inputSize, $hiddenSize, $outputSize, $learningRate = 0.1) {
        $this->learningRate = $learningRate;
        
        // Initialize random weights
        $this->weights = [
            'hidden' => $this->randomMatrix($inputSize, $hiddenSize),
            'output' => $this->randomMatrix($hiddenSize, $outputSize)
        ];
        
        $this->biases = [
            'hidden' => array_fill(0, $hiddenSize, 0),
            'output' => array_fill(0, $outputSize, 0)
        ];
    }
    
    public function predict($input) {
        $hidden = $this->sigmoid($this->matrixMultiply($input, $this->weights['hidden']));
        $output = $this->sigmoid($this->matrixMultiply($hidden, $this->weights['output']));
        return $output;
    }
    
    private function sigmoid($x) {
        return 1 / (1 + exp(-$x));
    }
}

class TextPreprocessor {
    private $stopWords;
    
    public function __construct($stopWords = []) {
        $this->stopWords = array_flip($stopWords ?: $this->getDefaultStopWords());
    }
    
    public function preprocess($text) {
        // Convert to lowercase
        $text = mb_strtolower($text, 'UTF-8');
        
        // Remove special characters but keep spaces
        $text = preg_replace('/[^\p{L}\p{N}\s]/u', ' ', $text);
        
        // Tokenize
        $tokens = preg_split('/\s+/', trim($text));
        
        // Remove stop words
        $tokens = array_filter($tokens, function($word) {
            return !isset($this->stopWords[$word]) && strlen($word) > 2;
        });
        
        return array_values($tokens);
    }
    
    public function getNGrams($tokens, $n = 2) {
        $ngrams = [];
        for ($i = 0; $i <= count($tokens) - $n; $i++) {
            $ngrams[] = array_slice($tokens, $i, $n);
        }
        return $ngrams;
    }
}

class RecommendationEngine {
    private $userItemMatrix = [];
    
    public function addRating($userId, $itemId, $rating) {
        $this->userItemMatrix[$userId][$itemId] = $rating;
    }
    
    public function cosineSimilarity($user1, $user2) {
        $user1Ratings = $this->userItemMatrix[$user1] ?? [];
        $user2Ratings = $this->userItemMatrix[$user2] ?? [];
        
        $commonItems = array_intersect_key($user1Ratings, $user2Ratings);
        
        if (empty($commonItems)) {
            return 0;
        }
        
        $dotProduct = 0;
        $norm1 = 0;
        $norm2 = 0;
        
        foreach ($commonItems as $item => $rating) {
            $dotProduct += $user1Ratings[$item] * $user2Ratings[$item];
            $norm1 += $user1Ratings[$item] ** 2;
            $norm2 += $user2Ratings[$item] ** 2;
        }
        
        return $dotProduct / (sqrt($norm1) * sqrt($norm2));
    }
    
    public function recommend($userId, $limit = 5) {
        $similarities = [];
        
        foreach ($this->userItemMatrix as $otherUser => $ratings) {
            if ($otherUser !== $userId) {
                $similarities[$otherUser] = $this->cosineSimilarity($userId, $otherUser);
            }
        }
        
        arsort($similarities);
        
        // Get recommendations from similar users
        $recommendations = [];
        $userRatings = $this->userItemMatrix[$userId] ?? [];
        
        foreach ($similarities as $similarUser => $similarity) {
            if ($similarity > 0.5) {
                foreach ($this->userItemMatrix[$similarUser] as $item => $rating) {
                    if (!isset($userRatings[$item]) && $rating >= 4) {
                        $recommendations[$item] = ($recommendations[$item] ?? 0) + $similarity * $rating;
                    }
                }
            }
        }
        
        arsort($recommendations);
        return array_slice(array_keys($recommendations), 0, $limit);
    }
}