AI-102 Practice Question: Implement natural language processing solutions
You are developing a custom text classification model using Azure AI Language. You have labeled 2000 documents across 10 categories. You need to evaluate the model's performance before deploying to production. Which THREE metrics should you examine?
⚠ Common exam trap
Candidates often confuse metrics from other NLP tasks (like speech recognition or translation) with classification metrics, leading them to select Word Error Rate or BLEU Score instead of the standard classification triad of precision, recall, and F1 score.
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
✓
Recall
Recall is correct because it measures the proportion of actual positive instances correctly identified by the model, which is critical in custom text classification to ensure that relevant documents are not missed. In Azure AI Language, recall helps assess how well the model captures all instances of each category, especially when class distribution is imbalanced across the 10 categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Measures the proportion of actual positives correctly identified.
- ✗
Word Error Rate
Why it's wrong here
Used for speech recognition.
- ✗
BLEU Score
Why it's wrong here
Used for evaluating machine translation.
- ✓
F1 Score
Why this is correct
Harmonic mean of precision and recall.
- ✓
Precision
Why this is correct
Measures the proportion of correctly predicted positive observations.
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