Ngân hàng câu hỏi

Consider time series analysis, regression models, and reinforcement learning; pay attention to features and data preprocessing.

Discuss memory pool allocation, handling fragmentation, and optimizing for allocation/deallocation speed.

Focus on handling high network traffic, efficient state synchronization, latency reduction, and scalability.

Address challenges in data distribution, replication, fault tolerance, consistency, and performance.

Implement distributed searching algorithms like MapReduce for scalability and efficiency.

Utilize stream processing frameworks (like Apache Kafka, Spark Streaming), ensure fault tolerance, and manage backpressure.

Focus on low latency, high throughput, reliable data feeds, order execution systems, and concurrent algorithms.

Use convolutional neural networks, pay attention to dataset quality and preprocessing, and handle class imbalances.

Implement advanced AI techniques like Monte Carlo Tree Search, deep learning, and reinforcement learning.

Use machine learning for anomaly detection, implement rule-based systems for known fraud patterns, ensure real-time processing.