Implement natural language processing solutions →hardMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement natural language processing solutions

Exhibit

Refer to the exhibit.
{
  "version": "2.0",
  "analysis": {
    "entities": [
      {
        "category": "Person",
        "text": "John Doe",
        "offset": 0,
        "length": 8,
        "confidenceScore": 0.99
      },
      {
        "category": "Person",
        "text": "Jane Smith",
        "offset": 20,
        "length": 10,
        "confidenceScore": 0.45
      }
    ]
  }
}

You are analyzing a document using Azure Cognitive Service for Language named entity recognition. The exhibit shows a partial JSON response for entity extraction. The engineer notices that 'Jane Smith' has a low confidence score of 0.45. Which action should the engineer take to improve the confidence score for similar entities?

⚠ Common exam trap

It's easy for candidates to assume prebuilt NER models can be retrained with labeled examples (Option A), but Azure Cognitive Service for Language's prebuilt NER is a fixed, non-trainable model, and custom retraining requires a separate Custom NER feature.

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

✓

Provide more context around the entity in the input text, such as titles or roles.

Providing more context around the entity, such as titles or roles (e.g., 'Dr. Jane Smith' or 'CEO Jane Smith'), gives the prebuilt named entity recognition (NER) model additional linguistic cues that improve its confidence in classifying the entity. Azure Cognitive Service for Language's NER uses a pre-trained model that does not support retraining with custom labels; instead, it relies on surrounding context to disambiguate entities. Adding descriptive terms helps the model leverage its training on patterns where such context correlates with higher confidence scores.

Answer analysis

Option-by-option breakdown

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

✗
Retrain the entity recognition model with labeled examples of 'Jane Smith'.
Why it's wrong here
The prebuilt model does not support retraining.
✓
Provide more context around the entity in the input text, such as titles or roles.
Why this is correct
Additional context helps the model disambiguate.
✗
Use a different language detection model to improve entity recognition.
Why it's wrong here
Language detection is unrelated to entity confidence.
✗
Decrease the confidence threshold to 0.3 to include 'Jane Smith' as a valid entity.
Why it's wrong here
This does not improve confidence; it only includes low-confidence entities.

Go deeper

AI-102 Practice Question: Implement natural language processing solutions

Exhibit

Why each option matters

Option-by-option breakdown

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