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.

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.

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.

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.

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.

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.

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.

Serialization is the process of converting a trained machine learning model into a format that can be used by production systems. It involves saving the model's parameters, architecture, and weights in a portable format so that it can be loaded and utilized for making predictions in real-time applications.

Min-Max normalization can be sensitive to outliers. If outliers are present in the data, this scaling method can compress the majority of data points into a narrow range, making it less suitable for preserving the information in the presence of outliers. In scenarios where outliers are a concern, alternative scaling methods like Robust Scaling may be preferred.

Apache Kafka is a distributed streaming platform that is well-suited for storing and processing large amounts of log data efficiently, making it a top choice for real-time data streaming and analysis.

The correct term is "Hierarchical Clustering." In unsupervised learning, clustering is a method used to group similar items or data points into sets or clusters based on their similarities. Hierarchical clustering is one of the techniques for this purpose. It creates a tree-like structure (dendrogram) to represent the relationships between data points, making it easier to identify groups of similar items.

Model monitoring is the process of continuously tracking the performance and behavior of a deployed machine learning model. It involves checking for deviations, evaluating predictions against real-world data, and ensuring that the model remains accurate and reliable over time. Monitoring is crucial for maintaining model quality in production.