Process a word in the context of the rest of a long sentence.
Explain a Transformer concept.
Choose two correct statements.
Attention weights relationships among tokens, and Transformer architectures use it to build contextual representations; limits and factuality still require evaluation.
Detailed explanation
Contextual combinations can be learned.
Contextual combinations can be learned.
They fit large-scale pretraining.
They fit large-scale pretraining.
Representation and factual accuracy differ.
Representation and factual accuracy differ.
Input and compute constraints remain.
Input and compute constraints remain.
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、Transformer、Attentionを確認する。Expected result
Attentionの役割と、能力・知識・上限を混同しない説明ができる。Key points
- Attention
- Transformer
- Context
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.