Explanation:

Comprehensive Detailed Explanation
There are two main types of data workloads: Batch and Streaming.
Batch Workload
Processes data in large groups (batches) at scheduled intervals.
Common when real-time is not required.
Examples: daily sales reports, hourly data refreshes, periodic ETL (Extract, Transform, Load).
Streaming Workload
Processes data in real time or near real time as it arrives.
Used for scenarios where immediate insights or actions are required.
Examples: fraud detection, IoT sensor monitoring, live purchase tracking.
Applying to the scenarios:
"Data for a product catalog will be loaded every 12 hours to a data warehouse." This is scheduled, not continuous. # Batch
"Thousands of data sets per second for online purchases will be loaded into a data warehouse in real time." This requires real-time processing. # Streaming
"Updates to inventory data will be loaded to a data warehouse every 1 million transactions." Data is grouped and loaded after reaching a threshold (1 million). # Batch Correct Matches:
Product catalog (12 hours) # Batch
Online purchases (real time) # Streaming
Inventory data (1 million transactions) # Batch
Microsoft References
Batch processing vs. Stream processing in Azure
Azure Stream Analytics overview
Data processing approaches: Batch vs Real-time