Question 852 of 988

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

Quick Answer

The correct approach is to use a single Azure AI Search pipeline with a skillset that includes the Document Layout skill, Text Translation skill, Sentiment skill, and Key Phrase Extraction skill, then enable semantic search. This works because a unified skillset processes all document types—PDFs, Word, and plain text—via the Document Layout skill, which handles layout-aware extraction without needing separate Document Intelligence for reviews. The Text Translation skill converts Spanish to English before sentiment and key phrase analysis, ensuring the multi-source document enrichment with translation and sentiment in Azure AI Search pipeline meets every requirement with minimal development effort. On the AI-102 exam, this tests your ability to consolidate cognitive skills into a single enrichment pipeline rather than overcomplicating with redundant services. A common trap is splitting into two pipelines for structured and unstructured data, which adds unnecessary overhead. Memory tip: think “One pipeline to rule them all”—a single skillset with Layout, Translate, Sentiment, and Key Phrases covers extraction, language, and analysis in one flow.

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 machine learning engineer at a retail company. The company wants to build a product knowledge base by extracting information from product manuals, specifications sheets, and customer reviews. The data sources include PDFs, Word documents, and plain text files stored in Azure Blob Storage. The solution must: (1) extract product name, model number, price, and key features; (2) analyze customer reviews to extract sentiment and common issues; (3) enable natural language queries like 'Which products have the best reviews under $100?'; (4) handle documents in English and Spanish. You need to design a solution using Azure AI Search and Azure AI Services. Which approach meets all requirements with the least development effort?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Use Azure AI Document Intelligence custom model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.
Why wrong: Requires two pipelines; no translation for Spanish.
B
Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.
Single pipeline handles all document types, translates, extracts sentiment, and enables natural language queries.
C
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.
Why wrong: OCR is not needed for digital documents; Entity Recognition may not extract key features well.
D
Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.
Why wrong: Document Intelligence may not handle plain text files well; no sentiment analysis.

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 a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.

Option D is correct because one skillset can handle all document types (using Document Layout skill), translate Spanish to English, extract sentiment from reviews, and extract key phrases for features. Document Intelligence is not needed for reviews. Option A misses sentiment analysis. Option B uses two pipelines unnecessarily. Option C misses translation.

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 model to extract product info from manuals/specs. Use a separate Azure AI Search pipeline for customer reviews with sentiment analysis. Enable semantic search.
Why it's wrong here
Requires two pipelines; no translation for Spanish.
✓
Use a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search.
Why this is correct
Single pipeline handles all document types, translates, extracts sentiment, and enables natural language queries.
Clue confirmation
The clue words "best", "least" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill (for scanned PDFs), Text Translation skill, Sentiment skill, and Entity Recognition skill. Enable semantic search.
Why it's wrong here
OCR is not needed for digital documents; Entity Recognition may not extract key features well.
✗
Use Azure AI Document Intelligence to extract product info from all documents, then feed into Azure AI Search. Enable semantic search.
Why it's wrong here
Document Intelligence may not handle plain text files well; no sentiment analysis.

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 a single Azure AI Search pipeline with a skillset that includes Document Layout skill, Text Translation skill (to English), Sentiment skill, and Key Phrase Extraction skill. Enable semantic search. — Option D is correct because one skillset can handle all document types (using Document Layout skill), translate Spanish to English, extract sentiment from reviews, and extract key phrases for features. Document Intelligence is not needed for reviews. Option A misses sentiment analysis. Option B uses two pipelines unnecessarily. Option C misses translation.

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

Are there clue words in this question I should notice?

Yes — watch for: "best", "least". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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