300-610 · Question #319
Which statement describes differences between training and inference in AI learning models?
The correct answer is D. The training process is more computationally demanding because it processes new data.. Training ingests and processes massive datasets through many iterations (forward and backward passes), requiring far more compute resources than inference. Inference, by contrast, uses the finalized model to make predictions and is optimized for low‑latency execution.
Question
Which statement describes differences between training and inference in AI learning models?
Options
- AThe training process is more optimized for low latency, which enables faster analysis of large
- BThe inference process requires continuous model adjustments to maintain accuracy with new
- CThe inference process requires high throughput to enable rapid training on large datasets.
- DThe training process is more computationally demanding because it processes new data.
How the community answered
(27 responses)- A4% (1)
- B4% (1)
- D93% (25)
Explanation
Training ingests and processes massive datasets through many iterations (forward and backward passes), requiring far more compute resources than inference. Inference, by contrast, uses the finalized model to make predictions and is optimized for low‑latency execution.
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