Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Your company has a large collection of legal contracts in PDF format stored in Azure Blob Storage. You need to extract key clauses, parties, and effective dates using a custom model in Azure AI Document Intelligence. The model must be retrained monthly as new contract templates are added. What is the recommended approach to handle model versioning and retraining?
⚠ Common exam trap
Many candidates assume retraining from scratch (Option C) is the only way to incorporate new data, overlooking the 'compose' operation and model copying features that enable incremental updates without losing prior training.
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
✓
Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples
Azure AI Document Intelligence supports model versioning through the 'compose' operation, which allows you to combine multiple trained models into a single composed model, or by copying an existing model and retraining it with new samples. This approach preserves the existing model's knowledge while incrementally updating it with new contract templates, avoiding the need to retrain from scratch each month.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples
Why this is correct
Model composition allows building on top of existing models.
- ✗
Use a multi-model ensemble by training separate models per template
Why it's wrong here
Not the recommended approach; a single model can handle multiple templates.
- ✗
Retrain the model from scratch each month using all historical data
Why it's wrong here
Inefficient and could lose previously learned patterns.
- ✗
Use Azure Machine Learning pipelines to automate retraining and deploy a new endpoint
Why it's wrong here
Not directly supported for Document Intelligence custom models.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 exam.