Home / AWS Certified AI Practitioner (AIF-C01) / 2 Practice / AWS-AIF-458
A classifier's output format varies.
Add a few examples.
Choose two practices.
Hint 1: Focus on the requirement Select accurate, representative, and boundary examples, then test order, count, distribution, leakage, bias, and token cost.
Hint 2: Key terms Review few-shot example design.
Few-shot promptingGuiding a model with a small number of input and output examples.
Boundary exampleA case near a decision, format, or policy boundary.
Example representativenessHow well examples reflect expected users, inputs, and edge cases.
Prompt token costThe amount of model context consumed by instructions and examples. × Incorrect Use accurate, representative, boundary, and prohibited examples, and test order, count, distribution, memorization, bias, and token cost.
Detailed explanation A Incorrect Put secrets or raw customer data into examples
Leakage and reuse risk increase.
B Incorrect One success example generalizes to every input
Coverage is insufficient.
C Correct Vary order, count, and distribution and measure bias, memorization, and cost
Example side effects are tested.
D Correct Use representative, accurate examples including boundaries and prohibited formats
Expected behavior is concrete.
Check official documentation Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 2.3のfew-shotプロンプト、例示、トークン管理を確認する。
Official references Official documentation 1 Official documentation 2 What to confirm 少数例の選び方と、例による偏り・暗記・費用を評価できる。
Key points Few-shot Boundary Representativeness 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 Few-shot prompting Guiding a model with a small number of input and output examples.
Boundary example A case near a decision, format, or policy boundary.
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