Heatmaps are effective for presenting complex relationships, making them suitable for displaying patient demographic data alongside treatment outcomes. Stacked Bar Charts and Scatter Plots may not provide the same level of clarity in this scenario, and Dual-Axis Charts are generally used for comparing two different scales.

Z-score normalization is a robust technique for handling outliers by scaling the data based on its mean and standard deviation. It identifies and mitigates the impact of outliers on the dataset.

JSON (JavaScript Object Notation) is commonly used to transmit data between a server and a client in API communication due to its lightweight and human-readable format. XML is an alternative, but JSON is more widely adopted in modern APIs. CSV and HTML are not typical formats for API data transmission.

Serverless computing simplifies infrastructure management by abstracting away server-related tasks. It allows data analysts to focus on code and analysis without the need to manage servers directly.

'Return on Investment' (ROI) is a financial metric that evaluates the efficiency of an investment. It is calculated by dividing the net profit from the investment by the initial investment cost. In this context, the blank should be filled with "Investment."

In the context of a large-scale data warehouse, prioritizing parallel processing can significantly enhance ETL efficiency by enabling the simultaneous processing of multiple data tasks. This reduces overall processing time and enhances system performance.

In a healthcare analytics dashboard, a Choropleth map can be used to visualize the geographical distribution of patient data. Choropleth maps use color variations to represent values across geographic regions, making them ideal for displaying spatial patterns in data.

Key Performance Indicators (KPIs) are crucial in dashboard design for enabling users to focus on key metrics at a glance. KPIs provide a quick overview of important measures, allowing users to assess performance without delving into detailed reports.

Time Series Analysis is commonly used in data mining to predict future trends based on historical data. It involves analyzing and modeling data points over time to identify patterns and make predictions. Dimensionality Reduction, Association Rule Mining, and Support Vector Machines serve different purposes in data mining.

Smoothing is a technique used in time series data to handle missing or irregular data points. It involves reducing noise and capturing underlying patterns. Interpolation, extrapolation, and resampling are other techniques used in time series analysis.