Which combination of factors is most widely credited for enabling the dramatic performance improvements in deep learning models over the past decade?
- AAvailability of large datasets, GPU-based parallel compute, and advances in model architectures Correct
- BFaster internet connectivity, improved operating systems, and reduced hardware costs
- CAdoption of relational databases, faster CPUs, and improved compiler toolchains
- DWider use of edge devices, lower memory prices, and growth in mobile applications
Why A is correct: These three pillars - big data, parallel GPU compute, and algorithmic advances such as the transformer architecture - are the foundational reasons deep learning achieved its recent breakthroughs.
Why B is wrong: Network speed and OS improvements are enabling infrastructure factors but not the core technical pillars. They do not directly drive model quality or training capability.
Why C is wrong: Relational databases and CPU speed gains did not drive deep learning progress. The shift to parallel GPU compute specifically unlocked the scale needed for modern AI workloads.
Why D is wrong: Edge devices and mobile apps are consumers of AI, not drivers of its foundational improvement. Lower memory prices alone do not account for architectural breakthroughs or training-scale gains.