The primary goal of EDA is to summarize and explore data. It involves visualizing and understanding the dataset's main characteristics and relationships before diving into more advanced tasks, such as model building or predictions. EDA helps identify patterns and anomalies in the data.

The primary characteristic that differentiates Big Data from traditional datasets is "Variety." Big Data often includes a wide range of data types, including structured, unstructured, and semi-structured data, making it more diverse than traditional datasets.

"Data-driven decision-making" underscores the significance of using data to inform decisions. It implies that decisions should be backed by data and analysis rather than relying solely on intuition. This approach enhances the quality and reliability of decision-making.

Recall is particularly useful when dealing with imbalanced datasets because it measures the ability of a model to identify all relevant instances of a class. In such scenarios, accuracy can be misleading, as the model may predict the majority class more frequently, resulting in a high accuracy but poor performance on the minority class. Recall, also known as true positive rate, focuses on capturing as many true positives as possible.

ARIMA is a time series forecasting method that utilizes past observations to predict future values. It incorporates autoregressive and moving average components, making it suitable for analyzing time series data. The other options are not specifically designed for time series forecasting and do not rely on past observations in the same way.

Unsupervised Learning is the type where the model discovers patterns or structures without prior data labeling. Common tasks in this category include clustering and dimensionality reduction, helping find hidden insights in data without any guidance.

Long Short-Term Memory (LSTM) is a variant of RNN that is designed to address the vanishing and exploding gradient problem. LSTMs use specialized gating mechanisms to better capture long-term dependencies in data, making them suitable for sequences with long-term dependencies.

In this scenario, minimizing false negatives is critical, as failing to detect fraud has a higher cost. Recall (Option B) focuses on minimizing false negatives, making it the most crucial metric to optimize in this context. While precision is important, the emphasis here is on avoiding false negatives. F1 Score balances precision and recall but may not prioritize minimizing false negatives. Accuracy is not the most relevant metric.

RNNs (Recurrent Neural Networks) have a hidden state that allows them to maintain a form of memory from previous inputs. This hidden state is crucial for processing sequences and time-series data, making them different from feedforward neural networks.

Forward Fill (FFill) is a method in time series data analysis used to fill missing values by taking the value of the nearest previous data point. This is often used when there's a trend in the data and using the average of nearby points makes sense.