2.1. AI Workload Categories

💡 First Principle: Every AI system is optimized for a specific input type. The algorithms that process images cannot process text—the math is fundamentally different. Images are grids of pixels, text is sequences of tokens, and audio is waveforms. This separation isn't arbitrary; it's architectural necessity.

What breaks without this categorization: On the exam, you'll see a scenario and four service options. Without understanding workload categories, every option looks plausible. With this framework, you identify the INPUT type and immediately know which category applies—eliminating three wrong answers before you even read them.

Imagine workload categories like hospital departments. Emergency, cardiology, and radiology exist separately because each requires different equipment and expertise. You don't send a broken arm to cardiology—and you don't send an image to an NLP service. For instance, consider a company that wants to "extract invoice data from scanned documents." Is that NLP or Computer Vision? The input is an IMAGE (scanned document), so it's Computer Vision (OCR)—even though the output is text. What input type does your scenario involve? Answer that question, and you've identified the workload category.

Building on the Input Modality Framework from Section 1.2.1, let's examine each workload category in detail.