- A
Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples
Model composition allows building on top of existing models.
- B
Use a multi-model ensemble by training separate models per template
Why wrong: Not the recommended approach; a single model can handle multiple templates.
- C
Retrain the model from scratch each month using all historical data
Why wrong: Inefficient and could lose previously learned patterns.
- D
Use Azure Machine Learning pipelines to automate retraining and deploy a new endpoint
Why wrong: Not directly supported for Document Intelligence custom models.
Question 645 of 993
Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
The 'compose' operation in Azure AI Document Intelligence allows you to combine up to 100 trained custom models into a single composed model, which is ideal for handling multiple contract templates. When retraining monthly, you can train a new model on the latest templates and compose it with the existing composed model, ensuring that the system recognizes both old and new templates without losing prior training. Under the hood, the composed model uses a routing mechanism that selects the best-matching sub-model for each document, providing seamless versioning without manual intervention.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Train a new model version using the 'compose' operation or copy the existing model and retrain with new samples — Option A is correct because 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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