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AI-102 Plan and manage an Azure AI solution Practice Question

You need to monitor the performance of an Azure AI Language service custom entity recognition model. Which metric should you track to evaluate the model's ability to correctly identify entities?

⚠ Common exam trap

Candidates often confuse accuracy (a common metric in classification) with the specialized F1 score required for entity recognition, where class imbalance makes accuracy a poor indicator of model performance.

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

✓

F1 score

The F1 score is the standard metric for evaluating custom entity recognition models in Azure AI Language, as it balances precision (correctly identified entities) and recall (missed entities). Unlike accuracy, which can be misleading due to class imbalance in entity labeling, F1 provides a harmonic mean that reflects the model's ability to correctly identify entities without bias toward the majority class.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Throughput
Why it's wrong here
Measures requests per second.
✗
Response latency
Why it's wrong here
Performance metric, not accuracy.
✓
F1 score
Why this is correct
Harmonic mean of precision and recall for entity recognition.
✗
Accuracy
Why it's wrong here
Can be misleading if entities are rare.

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AI-102 Plan and manage an Azure AI solution Practice Question

Why each option matters

Option-by-option breakdown

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