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AIF-C01 practice question: Training and fine-tuning (1 of 4)

Applications of Foundation Models A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A research lab is planning the lifecycle of a new foundation model, from the first training run to the feedback it collects after release.

Put these lifecycle stages in order, first stage first.

Put these 5 steps in order.

  1. A. Fine-tune the model on smaller, task-specific datasets.
  2. B. Collect feedback from real use to guide the next improvements.
  3. C. Pre-train the model on a very large, broad dataset.
  4. D. Deploy the model so that applications can call it.
  5. E. Evaluate the model against benchmarks and the lab's requirements.
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Correct order: C, A, E, D, B

  1. C. Pre-train the model on a very large, broad dataset. First: the lifecycle starts with a broad pre-training run on a very large dataset, which gives the model general language or visual knowledge.
  2. A. Fine-tune the model on smaller, task-specific datasets. Second: fine-tuning adapts the broadly trained model to specific tasks, which happens after the large pre-training run.
  3. E. Evaluate the model against benchmarks and the lab's requirements. Third: before release, the model is measured against benchmarks and the lab's own requirements to confirm it is fit for use.
  4. D. Deploy the model so that applications can call it. Fourth: once the model has been tuned and checked, it is deployed so applications can send it requests.
  5. B. Collect feedback from real use to guide the next improvements. Last: feedback from users shows where the deployed model falls short, and it guides the next round of work.

Explanation

A foundation model begins with broad pre-training, is then adapted to tasks through fine-tuning, is evaluated against requirements, and is deployed. Feedback from real use closes the loop and feeds the next cycle. This question tests the order of the foundation model lifecycle.

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