The main challenge addressed by the transformer architecture is capturing long-range dependencies in sequential data. Transformers use self-attention mechanisms to understand the relationship between distant words in a sentence, making them effective for various NLP tasks like machine translation and text summarization.

Relational databases are designed for storing structured data with well-defined rows and columns. This structured format allows for efficient storage and querying of data. Unstructured data, on the other hand, lacks a predefined structure.

To prevent SQL injection, you should sanitize and parameterize user inputs in your queries. This involves validating and escaping user input data to ensure that it cannot be used to execute malicious SQL commands. Other options, while important, do not directly prevent SQL injection.

The primary challenge of training very deep neural networks without specialized techniques is the vanishing gradient problem. As gradients are back-propagated through numerous layers, they can become extremely small, leading to slow convergence and making it difficult to train deep networks. Vanishing gradients hinder the ability of earlier layers to update their weights effectively.

Robust Scaling is less influenced by outliers because it scales the data based on the interquartile range (IQR) rather than the mean and standard deviation. This makes it a suitable choice when dealing with datasets that contain outliers.

Backpropagation is the process of adjusting the weights of a neural network to minimize the error between predicted and actual values. It is a fundamental training algorithm for neural networks, and it involves calculating gradients and updating weights to optimize the network's performance.

The Hadoop Distributed File System (HDFS) is designed for high availability and fault tolerance. It achieves this by replicating data blocks across multiple nodes in a distributed cluster, ensuring data integrity and reliable data storage. This is a fundamental feature of Hadoop's file system.

Supervised Learning with Convolutional Neural Networks (CNNs) is the optimal choice for image classification tasks like pedestrian detection. CNNs are designed for such tasks, while the other options are not suitable for image classification. Apriori is used for association rule mining, reinforcement learning for decision-making, and DBSCAN for clustering.

Apache Spark offers an optimized engine that supports real-time computations. This capability enables faster data analytics by allowing Spark to process data as it arrives, making it suitable for real-time data processing and analytics tasks. This is a key advantage of Spark over traditional batch processing systems.

The median is the middle value in a dataset when it's ordered. It's a measure of central tendency that's not affected by extreme values (outliers). To find the median, you arrange the data in ascending order, and if there's an even number of values, it's the average of the two middle values.