Explain how a foundation model is built and used.
Separate large-scale learning from answering a user input.
Choose two correct statements.
Pretraining learns general patterns from large data, while inference computes an output from a trained model; ordinary inference does not update weights.
Detailed explanation
It forms a model's base capabilities.
It forms a model's base capabilities.
It produces a response at use time.
It produces a response at use time.
Inference and additional training are different.
Inference and additional training are different.
Bias and errors can affect capabilities and limits.
Bias and errors can affect capabilities and limits.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 2.1の基盤モデル、事前学習、推論を確認する。Expected result
学習時の処理と利用時の推論を区別して説明できる。Key points
- Pretraining
- Inference
- Weights
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.