- A
Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service
Why wrong: Invoice model is not for research papers; Bot Service is not needed.
- B
Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification
Custom models can handle domain-specific extraction and classification.
- C
Azure AI Search's built-in OCR skill and a custom skill using Azure Functions
Why wrong: OCR extracts text but not structured fields; custom skill is possible but not the best combination.
- D
Azure AI Language's pre-built entity extraction and Azure AI Search
Why wrong: Pre-built models may not capture domain-specific entities.
Question 391 of 993
Implement knowledge mining and information extraction solutions →mediumMultiple 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 team is building a knowledge mining solution for research papers. You need to automatically categorize papers into topics and extract author names, publication dates, and references. The solution must use custom models because the papers are domain-specific. Which combination of Azure services should you use?
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
✓
Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification
Option B is correct because it combines Azure AI Document Intelligence's custom extraction model to extract domain-specific fields like author names, publication dates, and references, with Azure AI Language's custom text classification to categorize research papers into topics. This pairing directly addresses the need for custom models tailored to the specialized domain, unlike pre-built or generic solutions.
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.
- ✗
Azure AI Document Intelligence's pre-built invoice model and Azure Bot Service
Why it's wrong here
Invoice model is not for research papers; Bot Service is not needed.
- ✓
Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification
Why this is correct
Custom models can handle domain-specific extraction and classification.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure AI Search's built-in OCR skill and a custom skill using Azure Functions
Why it's wrong here
OCR extracts text but not structured fields; custom skill is possible but not the best combination.
- ✗
Azure AI Language's pre-built entity extraction and Azure AI Search
Why it's wrong here
Pre-built models may not capture domain-specific entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common mistake on the Azure AI-102 exam is choosing pre-built models (like the invoice model or generic entity extraction) thinking they can be adapted to domain-specific needs, but the question explicitly requires custom models. Always verify if the scenario demands custom training.
Detailed technical explanation
How to think about this question
Azure AI Document Intelligence's custom extraction model uses a neural network trained on labeled documents to extract key-value pairs and table data, supporting domain-specific fields via the 'custom' model type (API version 2023-07-31). Azure AI Language's custom text classification employs a fine-tuned transformer model (e.g., BERT) that can be trained on a small set of labeled documents to classify text into user-defined categories, using the 'CustomTextClassification' project type. In practice, this combination allows a pipeline where Document Intelligence extracts structured data, and Language classifies the document's topic, all without requiring custom coding for each field.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Azure AI Document Intelligence's custom extraction model and Azure AI Language's custom text classification — Option B is correct because it combines Azure AI Document Intelligence's custom extraction model to extract domain-specific fields like author names, publication dates, and references, with Azure AI Language's custom text classification to categorize research papers into topics. This pairing directly addresses the need for custom models tailored to the specialized domain, unlike pre-built or generic solutions.
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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