AI-103 Text Analysis Practice Question
You are preparing training data for a custom named entity recognition (NER) project in Language Studio. Which THREE guidelines should you follow to ensure high model accuracy? (Choose three.)
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
✓
Ensure consistent and accurate tagging of entities across all training documents.
Best practices for custom NER training data include providing a diverse set of documents, ensuring consistent tagging across all instances, and maintaining sufficient sample counts per entity type.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure consistent and accurate tagging of entities across all training documents.
Why this is correct
Consistency in labeling is critical for model training accuracy.
- ✗
Use exactly one training document for the entire project.
Why it's wrong here
A single document is insufficient for machine learning training.
- ✓
Provide sufficient examples for each entity type based on service guidelines.
Why this is correct
Sufficient samples per entity type ensure the model learns robust patterns.
- ✓
Include a diverse set of training documents that reflect real-world data distribution.
Why this is correct
Diverse training data improves model generalization.
- ✗
Tag every single word in every document as an entity.
Why it's wrong here
Tagging everything causes noise and degrades model precision.
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Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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