AIF-C01 Applications of Foundation Models Practice Question
A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text. Which TWO metrics are most appropriate for evaluating text quality?
⚠ Common exam trap
AWS often tests the distinction between classification metrics (precision, accuracy, F1) and generation evaluation metrics (perplexity, BLEU), leading candidates to mistakenly apply classification concepts to text quality assessment.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Perplexity
Perplexity measures how well a language model predicts a sample, with lower values indicating higher confidence and coherence in generated text. BLEU evaluates the overlap between generated text and reference text, making it suitable for assessing fluency and relevance in content generation tasks like marketing copy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision is for classification.
- ✓
Perplexity
Why this is correct
Perplexity measures how well the model predicts the text.
- ✗
Accuracy
Why it's wrong here
Accuracy is for classification tasks, not text generation.
- ✗
F1 score
Why it's wrong here
F1 score is for classification.
- ✓
BLEU (Bilingual Evaluation Understudy)
Why this is correct
BLEU measures n-gram overlap with reference text.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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