The correct answer is Azure AI Language and Azure AI Search. Azure AI Language provides the built-in cognitive skills for key phrase extraction, entity recognition, and sentiment analysis, while Azure AI Search handles the indexing and full-text search of the enriched content. To enrich a search index with Azure AI Language skills, you attach a skillset to your indexer that calls these native language analysis skills, which process the customer feedback documents during ingestion. On the AI-102 exam, this scenario tests your understanding of the cognitive skills pipeline and the specific roles of each Azure AI service. A common trap is selecting Azure AI Document Intelligence or Translator, but remember that Document Intelligence extracts text from images or PDFs, not language insights, and Translator handles language conversion, not extraction. The key distinction is that Language provides the analysis skills, and Search provides the storage and retrieval. Memory tip: Language analyzes, Search indexes—think “Language learns, Search returns.”
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 building a knowledge mining solution using Azure AI Search and Azure AI Language. The solution must extract key phrases, entities, and sentiment from customer feedback documents. After processing, the enriched content should be stored in the search index for full-text search. You need to configure the enrichment pipeline. Which two Azure AI services should you integrate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Language and Azure AI Search
Azure AI Language provides key phrase extraction, entity recognition, and sentiment analysis as built-in skills. Azure AI Search provides the indexing and search capabilities. Option A is wrong because Azure AI Translator is for translation, not the required analyses. Option B is wrong because Azure AI Document Intelligence is for extracting text from documents, not for language analysis. Option D is wrong because the custom skill would be redundant if native skills exist.
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.
✓
Azure AI Language and Azure AI Search
Why this is correct
Language provides the required skills; Search indexes the enriched content.
Related concept
Static NAT maps one inside address to one outside address.
✗
Azure AI Language and a custom skill in Azure Functions
Why it's wrong here
Custom skill is unnecessary; native skills exist.
✗
Azure AI Translator and Azure AI Search
Why it's wrong here
Translator is not needed for key phrases, entities, sentiment.
✗
Azure AI Document Intelligence and Azure AI Search
Why it's wrong here
Document Intelligence extracts text, not performs language 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.
Trap categories for this question
Keyword trap
Translator is not needed for key phrases, entities, sentiment.
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 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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
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.
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: Azure AI Language and Azure AI Search — Azure AI Language provides key phrase extraction, entity recognition, and sentiment analysis as built-in skills. Azure AI Search provides the indexing and search capabilities. Option A is wrong because Azure AI Translator is for translation, not the required analyses. Option B is wrong because Azure AI Document Intelligence is for extracting text from documents, not for language analysis. Option D is wrong because the custom skill would be redundant if native skills exist.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You are deploying a knowledge mining solution using Azure AI Search and Azure AI Document Intelligence. The solution must extract text from scanned documents, identify named entities, and index the content. You need to configure the skillset. Which TWO built-in skills should you include in the skillset?
medium
A.Merge skill
B.LanguageDetection skill
✓ C.OCR skill
✓ D.EntityRecognition skill
E.KeyPhraseExtraction skill
Why C: The OCR skill extracts text from scanned images. The EntityRecognition skill identifies named entities. The Merge skill is not required because OCR output is already text. The KeyPhraseExtraction skill extracts key phrases, not entities. The LanguageDetection skill detects language, not entities.
Last reviewed: Jun 20, 2026
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