Question 726 of 988

Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to combine Azure AI Document Intelligence’s layout model with OCR, a custom translation skill, and a scoring profile with freshness boosting. This combination directly meets the requirement to design an Azure Cognitive Search skillset for multilingual research reports with handwriting and tables because Document Intelligence’s layout model extracts text, table structures, and handwritten annotations via OCR, while a custom skill using the Translator service handles English, Spanish, and French. The scoring profile with freshness boosting then ensures the index prioritizes the most recent reports, satisfying the daily update and search relevance needs. On the AI-102 exam, this scenario tests your ability to select the right cognitive skills for unstructured document processing—a common trap is choosing Azure AI Vision OCR alone, which misses table extraction, or using the Language service, which lacks layout analysis. Remember the mnemonic “LOTF” for Layout, OCR, Translation, Freshness to recall the four critical components.

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 data engineer at a multinational corporation. The company has thousands of research reports in PDF format stored in Azure Blob Storage. The reports contain text, tables, charts, and handwritten annotations. Your team needs to build a knowledge mining solution using Azure AI Search that allows researchers to query the reports using natural language. The solution must extract text, table structures, and handwritten annotations. Additionally, the solution must handle multiple languages (English, Spanish, and French) and ensure that the index is updated daily as new reports are added. The search should prioritize the most recent reports. You have an Azure AI Search service in the S2 tier. Which combination of actions should you take to meet these requirements?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Use Azure AI Vision OCR skill for text extraction, add a translation skill, and use a simple search query
Why wrong: Azure AI Vision OCR does not extract table structure or handwriting well.
B
Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting
Document Intelligence extracts tables and handwriting; translation skill handles multilingual; scoring profile boosts recent docs.
C
Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly
Why wrong: Weekly schedule does not meet daily update requirement; read model may not extract tables.
D
Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking
Why wrong: Language service does not extract from PDFs or handle handwriting.

Full breakdown with real-world context →

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 layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting

Option B is correct. Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure. Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

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 Vision OCR skill for text extraction, add a translation skill, and use a simple search query
Why it's wrong here
Azure AI Vision OCR does not extract table structure or handwriting well.
✓
Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting
Why this is correct
Document Intelligence extracts tables and handwriting; translation skill handles multilingual; scoring profile boosts recent docs.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly
Why it's wrong here
Weekly schedule does not meet daily update requirement; read model may not extract tables.
✗
Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking
Why it's wrong here
Language service does not extract from PDFs or handle handwriting.

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 layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting — Option B is correct. Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure. Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

What should I do if I get this AI-102 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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