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.

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.

Biased algorithms can lead to unfair or discriminatory outcomes, as they may favor certain groups over others. This can have significant ethical and legal implications, causing harm to individuals and undermining trust in AI systems.

In CNNs, the layers that preserve the spatial relationships between pixels by learning image features through small squares of input data are called "Convolution" layers. These layers apply convolutional operations to extract features from the input data, preserving the local spatial relationships in the image.

While Apache Kafka, Apache Spark, and Hadoop MapReduce are often used for real-time or near-real-time data processing, MySQL is a traditional relational database system that is not designed for real-time processing.

The blank should be filled with "Convolutional." Convolutional layers are used in Convolutional Neural Networks (CNNs) to combine features across input data by applying convolution operations. This is essential for tasks like image recognition.

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.

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.

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 sigmoid activation function maps any input to a value between 0 and 1. It's commonly used in neural networks for binary classification problems and helps introduce non-linearity in the network's computations.