AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are designing a knowledge mining solution for a manufacturing company that needs to extract information from equipment maintenance manuals. The manuals are in multiple languages (English, French, German). You need to ensure that the extracted content is searchable in English only. Which approach should you use?
⚠ Common exam trap
AI-102 often tests the difference between enrichment skills that transform content (Translation) versus those that only extract metadata (Entity Recognition, Key Phrase Extraction), causing candidates to pick extraction skills when translation is required.
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 the Text Translation skill to translate all content to English during indexing.
Azure AI Search's Text Translation skill (backed by Azure AI Translator) translates non-English content into a target language during the enrichment pipeline, so all indexed content is normalized to English and searchable in English only. This directly satisfies the requirement to make multilingual manuals searchable in English. The skill runs at indexing time, so the index contains only English text.
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 the Entity Recognition skill to extract entities and then index entities only.
Why it's wrong here
Entity Recognition extracts named entities, not translated prose, so French and German manual text stays untranslated and unsearchable in English. It is tempting because entity extraction is genuinely useful for pulling structured values such as part numbers or dates into indexable fields.
- ✗
Use the Language Detection skill to identify language and then index all content as-is.
Why it's wrong here
Language Detection only labels each document's language; indexing content as-is leaves French and German text untranslated, so English queries cannot match it. It is tempting because detection is the correct first step when routing documents to per-language analysers rather than translating them.
- ✓
Use the Text Translation skill to translate all content to English during indexing.
Why this is correct
The Text Translation skill translates French and German content into English during indexing, so all extracted text is stored in English. This makes the index searchable in English only, regardless of the manuals' original languages.
- ✗
Use the Key Phrase Extraction skill to extract key phrases and then index them.
Why it's wrong here
Key Phrase Extraction surfaces salient terms in their source language, leaving French and German phrases untranslated, so English-only search fails. It is tempting because key phrase extraction is the right skill when you need to boost relevance or build tag-style facets over monolingual content.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.