The issue where adding more layers to a deep neural network exacerbates the training problem due to diminishing gradients is called "Vanishing Gradient." It occurs when gradients become too small during backpropagation, making it challenging for deep networks to learn intricate patterns in the data.

The "Transformation" stage in the ETL (Extract, Transform, Load) process is responsible for cleaning, validating, and transforming data to ensure its quality. This phase involves data cleaning, data type conversion, and other operations to make the data suitable for analysis and reporting.

When dealing with skewed datasets, the median is preferred for imputation. The median is robust to extreme values and is less affected by outliers than the mean. Using the median as the measure of central tendency helps maintain the integrity of the dataset in the presence of outliers.

Data Engineers are responsible for the efficient collection, storage, and processing of data. They create the infrastructure necessary for Data Scientists and Analysts to work with data effectively.

Min-Max Scaling, also known as feature scaling, is used to transform values within a specific range (typically 0 to 1) for different features. It ensures that variables with different scales have a similar impact on the analysis.

Exploratory Data Analysis (EDA) often employs dimensionality reduction techniques like Principal Component Analysis (PCA) to visualize data in lower-dimensional spaces (2 or 3 dimensions) for better understanding, hence the term "dimensions."

In CNNs, the operation that combines the outputs of neuron clusters and produces a single output for the cluster is called "Pooling." Pooling reduces the spatial dimensions of the feature maps, making them smaller and more computationally efficient while retaining important features.

The healthcare database contains a mix of structured data (name, age, diagnosis) and semi-structured data (scanned documents and doctor's notes). Semi-structured data includes elements with partial structure, like documents, which can be tagged or indexed for better retrieval.

The model is likely facing the issue of overfitting. Overfitting occurs when the model learns the training data too well, including noise, resulting in excellent performance on the training set but poor generalization to unseen data. It's an example of a high-variance problem in the bias-variance tradeoff. To address overfitting, techniques like regularization and more data are often used.

MongoDB is a NoSQL database that is ideal for handling hierarchical data and provides better scalability for unstructured data. MongoDB uses BSON (Binary JSON) format, which makes it a good choice for applications that require flexibility and scalability in dealing with complex data structures.