- A
Use ROUGE scores to compare model outputs
Why wrong: ROUGE is for summarization, not classification tasks like sentiment.
- B
Run a human evaluation study with 100 judges
Why wrong: Human evaluation is resource-intensive and may not be needed initially; automated metrics are more practical.
- C
Use BLEU scores to measure n-gram overlap with reference labels
Why wrong: BLEU is for translation, not sentiment analysis.
- D
Compute accuracy, precision, recall, and F1-score on the labeled test set
These standard classification metrics allow direct comparison of model performance on the task.
Question 32 of 1,000
mediumMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A team is evaluating two different foundation…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team is evaluating two different foundation models for a sentiment analysis task. They have a labeled test dataset. Which evaluation approach should they use to compare the models' performance on this task?
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
✓
Compute accuracy, precision, recall, and F1-score on the labeled test set
Option D is correct because accuracy, precision, recall, and F1-score are standard classification metrics that directly measure how well a model's predicted sentiment labels match the ground truth labels in a labeled test dataset. These metrics provide a quantitative, reproducible comparison of model performance on a supervised sentiment analysis task, unlike text-generation metrics or subjective human evaluation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use ROUGE scores to compare model outputs
Why it's wrong here
ROUGE is for summarization, not classification tasks like sentiment.
- ✗
Run a human evaluation study with 100 judges
Why it's wrong here
Human evaluation is resource-intensive and may not be needed initially; automated metrics are more practical.
- ✗
Use BLEU scores to measure n-gram overlap with reference labels
Why it's wrong here
BLEU is for translation, not sentiment analysis.
- ✓
Compute accuracy, precision, recall, and F1-score on the labeled test set
Why this is correct
These standard classification metrics allow direct comparison of model performance on the task.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between metrics for generative tasks (ROUGE, BLEU) versus classification tasks (accuracy, precision, recall, F1), leading candidates to mistakenly apply text-generation metrics to a classification problem.
Detailed technical explanation
How to think about this question
For sentiment analysis, models output discrete class labels (e.g., positive, negative, neutral), so the evaluation uses a confusion matrix to compute accuracy (overall correctness), precision (positive predictive value), recall (sensitivity), and F1-score (harmonic mean of precision and recall). In practice, imbalanced sentiment classes can make accuracy misleading, so macro- or weighted-averaged F1-scores are often preferred to ensure fair comparison across all classes.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Compute accuracy, precision, recall, and F1-score on the labeled test set — Option D is correct because accuracy, precision, recall, and F1-score are standard classification metrics that directly measure how well a model's predicted sentiment labels match the ground truth labels in a labeled test dataset. These metrics provide a quantitative, reproducible comparison of model performance on a supervised sentiment analysis task, unlike text-generation metrics or subjective human evaluation.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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