Question 768 of 988

Quick Answer

The correct combination is Document Layout skill, Entity Recognition skill, Custom Entity Lookup skill, and Key Phrase Extraction skill. This works because Document Layout skill is specifically designed to handle both typed and handwritten text within PDFs, while Entity Recognition extracts person names (parties, judge) and organizations, and a custom skill can parse citation patterns like 'Smith v. Jones' via pattern matching. On the AI-102 exam, this question tests your ability to select cognitive skills for knowledge mining solutions that enrich legal documents with metadata—a common scenario where you must distinguish between OCR and Document Layout, as the latter preserves document structure and handles mixed text types. A frequent trap is choosing OCR alone, but Document Layout is superior for complex layouts. For memory, think "DECLARE": Document Layout, Entity, Custom Lookup, and Key Phrase Extraction—the four pillars for legal document enrichment.

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.

You are a developer at a legal firm. The firm has a repository of court case documents stored as PDFs in Azure Blob Storage. You need to build a knowledge mining solution that enables lawyers to search for cases by parties involved, judge name, case number, date, and key legal topics. The documents are in English and contain both typed and handwritten text. The solution must extract the aforementioned metadata and also identify citations to other cases (e.g., 'Smith v. Jones'). You plan to use Azure AI Search with cognitive skills. Which combination of skills should you include in your skillset?

Question 1mediummultiple choice
Read the full NAT/PAT explanation →

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

Document Layout skill, Entity Recognition skill (for person names and organizations), Custom Entity Lookup skill (for case citation patterns), and Key Phrase Extraction skill for legal topics.

Option A is correct because Document Layout skill handles typed and handwritten text, Entity Recognition can extract person names (parties, judge) and law-related entities, and a custom skill can parse case citations. Option B misses entity extraction for parties and judge. Option C uses OCR but Document Layout is better. Option D uses Translator unnecessarily.

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.

  • OCR skill (to handle handwriting), Key Phrase Extraction skill, Sentiment skill, and Language Detection skill.

    Why it's wrong here

    Missing entity extraction for parties/judge; Sentiment not needed.

  • OCR skill, Document Layout skill, Entity Recognition skill, and Text Translation skill.

    Why it's wrong here

    Redundant OCR and Document Layout; translation not needed.

  • Document Layout skill, Entity Recognition skill (for person names and organizations), Custom Entity Lookup skill (for case citation patterns), and Key Phrase Extraction skill for legal topics.

    Why this is correct

    Covers all requirements: extraction of metadata, citations, and topics.

    Related concept

    Static NAT maps one inside address to one outside address.

  • Document Layout skill, Text Translation skill, Sentiment skill, and Key Phrase Extraction skill.

    Why it's wrong here

    Missing entity extraction; translation not needed.

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: Document Layout skill, Entity Recognition skill (for person names and organizations), Custom Entity Lookup skill (for case citation patterns), and Key Phrase Extraction skill for legal topics. — Option A is correct because Document Layout skill handles typed and handwritten text, Entity Recognition can extract person names (parties, judge) and law-related entities, and a custom skill can parse case citations. Option B misses entity extraction for parties and judge. Option C uses OCR but Document Layout is better. Option D uses Translator unnecessarily.

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.

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Last reviewed: Jun 20, 2026

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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.