- A
Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.
Best approach for structured extraction, PII redaction, and natural language query.
- B
Use Azure AI Vision OCR to extract text from PDFs, then use Azure AI Language to extract entities and key phrases. Index in Azure AI Search with semantic search.
Why wrong: OCR is not ideal for digital PDFs; no PII redaction.
- C
Use Azure AI Search with blob indexer, include a skillset with Document Layout skill, Entity Recognition skill (for financial entities), and Key Phrase Extraction. Enable semantic search.
Why wrong: No PII redaction; Entity Recognition may not extract custom financial data accurately.
- D
Use Azure OpenAI GPT-4 to process each report via a custom extraction prompt, then send extracted JSON to Azure AI Search. Enable semantic search.
Why wrong: GPT-4 may be costly and inconsistent for table extraction; lacks PII redaction.
Quick Answer
The correct combination is Azure AI Document Intelligence custom extraction model, a PII detection skill in the AI Search enrichment pipeline, and semantic search. This works because Document Intelligence is purpose-built for extracting structured fields like revenue and net income from tables in digital PDFs, while the PII detection skill redacts emails and phone numbers before indexing, and semantic search enables natural language queries like “highest revenue in 2023.” On the AI-102 exam, this scenario tests your ability to choose the right cognitive skill for document parsing versus OCR, and to remember that PII redaction must be a dedicated step in the enrichment pipeline—not an afterthought. A common trap is picking Azure OpenAI for extraction, but it lacks reliability for tabular financial data and has no built-in PII redaction. Memory tip: think “Document Intelligence for tables, PII skill for redaction, semantic search for questions”—that trio covers extraction, compliance, and querying in one pipeline.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
You are a solution architect at a financial services company. You need to implement a knowledge mining solution that extracts information from annual reports (PDF) of publicly traded companies. The reports contain financial tables, executive summaries, and legal disclaimers. The solution must: (1) extract the company name, fiscal year, revenue, net income, and CEO name; (2) redact any personally identifiable information (PII) like email addresses and phone numbers before indexing; (3) index the extracted data in Azure AI Search; (4) allow users to query using natural language (e.g., 'Which company had the highest revenue in 2023?'). The reports are uploaded to an Azure Blob Storage container. You have access to Azure AI Services and Azure OpenAI. Which combination of services and configurations 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
Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.
Option C is correct because it uses Document Intelligence for structured extraction (tables, financial data), PII detection skill for redaction, and semantic search for natural language queries. Option A lacks PII redaction. Option B uses OpenAI for extraction but may be less reliable for structured tables and lacks PII redaction. Option D uses OCR but Document Intelligence is better for digital PDFs.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries.
Why this is correct
Best approach for structured extraction, PII redaction, and natural language query.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use Azure AI Vision OCR to extract text from PDFs, then use Azure AI Language to extract entities and key phrases. Index in Azure AI Search with semantic search.
Why it's wrong here
OCR is not ideal for digital PDFs; no PII redaction.
- ✗
Use Azure AI Search with blob indexer, include a skillset with Document Layout skill, Entity Recognition skill (for financial entities), and Key Phrase Extraction. Enable semantic search.
Why it's wrong here
No PII redaction; Entity Recognition may not extract custom financial data accurately.
- ✗
Use Azure OpenAI GPT-4 to process each report via a custom extraction prompt, then send extracted JSON to Azure AI Search. Enable semantic search.
Why it's wrong here
GPT-4 may be costly and inconsistent for table extraction; lacks PII redaction.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use Azure AI Document Intelligence custom extraction model trained on annual reports to extract fields. In the Azure AI Search pipeline, add a PII detection skill to redact PII. Enable semantic search for natural language queries. — Option C is correct because it uses Document Intelligence for structured extraction (tables, financial data), PII detection skill for redaction, and semantic search for natural language queries. Option A lacks PII redaction. Option B uses OpenAI for extraction but may be less reliable for structured tables and lacks PII redaction. Option D uses OCR but Document Intelligence is better for digital PDFs.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
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Last reviewed: Jun 20, 2026
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.
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