For multi-class classification with 10 classes, the most suitable activation function for the output layer is Softmax, and the most suitable loss function is Cross-Entropy. Softmax provides class probabilities, and Cross-Entropy measures the dissimilarity between the predicted probabilities and the true class labels. This combination is widely used in classification tasks.

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.

Time-series data often has specific characteristics, such as seasonality and trends. LightGBM is well-suited for such data as it can handle categorical features efficiently and is capable of capturing complex patterns, making it a strong choice for time-series forecasting.

An event-driven database, such as Apache Kafka, is suitable for capturing and storing real-time activities and interactions, especially when the data is unstructured, and the schema might evolve over time.

For real-time object detection in computer vision, the most suitable technique is "Object Detection." It allows the system to identify and locate specific objects, such as pedestrians, traffic lights, and vehicles, in a given frame or scene. Image classification, semantic segmentation, and OCR serve different purposes.