2.1Standard
Reduce inference memory and cost.
Keep quality degradation within limits.
Choose two evaluations.
× Incorrect
Compare quality, latency, memory, and cost for each precision on the same cases, including safety, refusal, long-context, and multilingual regressions.
Detailed explanation
CorrectCompare quality, latency, and memory for precisions on the same evaluation set
Cost and quality can be balanced.
IncorrectLower precision always preserves quality and safety
Impact differs by model and task.
CorrectCheck safety, refusal, long-context, and multilingual regression cases
Average scores do not hide important failures.
IncorrectChoose by memory alone
User quality and safety are ignored.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 2.1のモデル性能、コスト、最適化を確認する。
Official references
What to confirm
量子化を品質と運用コストのトレードオフで評価できる。
Key points
- Quantization
- Memory
- Regression
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
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Quantization
Reducing the numeric precision used for model values to lower memory or computation needs.
Bit width
The number of bits used to represent a numeric model value.
Question IDAWS-AIF-320
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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