Convolutional Neural Networks (CNNs) are specifically designed for image classification tasks. They use convolutional layers to capture spatial hierarchies in the input data, making them highly effective for image recognition and analysis.

Fine-tuning in transfer learning involves taking a pre-trained model's architecture and training it with new data, adjusting the model's parameters to suit the specific task. It's a common technique for leveraging pre-trained models for custom tasks.

In a normal distribution, approximately 95% of the data falls within two standard deviations of the mean. This is a fundamental property of the normal distribution, as specified by the Empirical Rule or the 68-95-99.7 rule, which describes the percentage of data within one, two, and three standard deviations of the mean.

For real-time analysis on streaming data, Apache Spark is a powerful tool. It provides excellent performance capabilities and extensive libraries for stream processing, making it suitable for handling and analyzing large volumes of data in real-time.

In this scenario, you would use a content-based recommendation approach. It recommends items (in this case, movies) based on their content or features, such as genre, actors, and plot. Collaborative filtering and hybrid systems focus on user behavior and preferences, while popularity-based recommendations don't consider movie content.

One of the most popular algorithms used in collaborative filtering for recommender systems is Singular Value Decomposition (SVD). SVD is a matrix factorization technique that can be used to make recommendations based on user-item interactions.

InfluxDB is specifically designed for time-series data, making it a suitable choice for applications that need to efficiently store and query time-stamped data, such as IoT or monitoring systems. Its structure and optimizations are tailored for this use case.

Matrix factorization techniques primarily address the sparsity problem in recommender systems. In such systems, user-item interaction data is typically sparse, and matrix factorization helps in predicting missing values by factoring the observed data matrix into latent factors. This mitigates the sparsity challenge.

Latent Semantic Analysis (LSA) is an NLP technique that transforms text into a meaningful vector space by capturing latent semantic relationships between words. It helps in reducing the dimensionality of text data while preserving its meaning. The other options are not methods for transforming text into numerical vectors and serve different purposes in NLP and data analysis.

In Transformer architectures, the mechanism that allows the model to focus on different parts of the input data differently is known as "Self-Attention." It enables the model to weigh input elements based on their relevance for a given context.