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.

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.

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.

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.

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.

Data Science aims to collect and analyze data to gain insights and make data-driven decisions. While the other options are important aspects of Data Science, the primary goal is to gather and analyze data effectively.

Model versioning is a crucial aspect of model deployment. It enables organizations to update machine learning models without causing downtime. This is vital in real-world applications where models need to adapt to changing data and conditions.

Data points that are far from their cluster centroids are likely outliers. Outliers can significantly impact clustering results. To address this issue, you can consider different strategies such as removing outliers, using robust clustering algorithms, or applying feature scaling and normalization to make the clusters less sensitive to outliers.

The blank should be filled with "Sigmoid." The Sigmoid activation function maps input values to the range of 0 to 1. It can cause the vanishing gradient problem, which makes training deep networks difficult due to its derivative approaching zero for extreme input values.

In computer vision, detecting specific features or patterns in an image is often achieved using Convolutional Neural Networks (CNNs). CNNs are well-suited for image feature extraction and are widely used in tasks like object detection and image classification.

Overfitting in neural networks can be controlled by reducing the capacity of the network, which refers to the number of nodes and layers. A simpler network is less likely to overfit as it has fewer parameters to learn and generalize more effectively.