- A
Use Azure AI Language key phrase extraction, and enable vector search
Why wrong: Azure AI Language key phrase extraction does not extract structured entities like color, size, or brand. Vector search is for semantic similarity, not fuzzy matching for misspellings.
- B
Use Azure AI Document Intelligence to extract entities, and enable semantic ranking
Why wrong: Azure AI Document Intelligence is designed for extracting text and structure from documents, not for entity extraction from product descriptions in a database. Semantic ranking improves relevance but does not handle entity-based filtering or fuzzy matching.
- C
Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Azure AI Language entity extraction as a custom skill can extract attributes like color and brand from product descriptions. Fuzzy search in the index handles misspelled queries.
- D
Use Azure AI Vision OCR to extract text, and enable synonyms
Why wrong: Azure AI Vision OCR is used for extracting text from images, not for extracting entities from structured text. Synonyms may help with related terms but do not address fuzzy matching for misspellings.
AI-102 Entity extraction Practice Question
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. A key principle to apply: entity extraction. 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 an e-commerce company. The company wants to build a product search feature that allows customers to search for products using natural language phrases like "red running shoes under $100". The product catalog is stored in Azure Cosmos DB and includes product descriptions, prices, and categories. The solution must use Azure AI Search and must extract entities from product descriptions to enable filtering (e.g., color, size, brand). The search must also support fuzzy matching for misspelled queries. You need to design the indexing pipeline. Which actions should you take?
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 Language entity extraction as a custom skill, and enable fuzzy search in the index
Option C is correct. Using Azure AI Language entity extraction as a custom skill extracts attributes like color, size, and brand from product descriptions, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because key phrase extraction identifies topics, not specific entities like color or brand, and vector search is for similarity matching, not fuzzy matching for typos. Option B is incorrect because Azure AI Document Intelligence is designed to extract text from documents (e.g., PDFs, images), not to extract named entities from text already stored in a database; semantic ranking improves relevance but does not perform entity extraction. Option D is incorrect because Azure AI Vision OCR extracts text from images, not from product descriptions in Cosmos DB, and synonyms expand queries but do not extract entities needed for filtering.
Key principle: Entity extraction
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 Language key phrase extraction, and enable vector search
Why it's wrong here
Azure AI Language key phrase extraction does not extract structured entities like color, size, or brand. Vector search is for semantic similarity, not fuzzy matching for misspellings.
- ✗
Use Azure AI Document Intelligence to extract entities, and enable semantic ranking
Why it's wrong here
Azure AI Document Intelligence is designed for extracting text and structure from documents, not for entity extraction from product descriptions in a database. Semantic ranking improves relevance but does not handle entity-based filtering or fuzzy matching.
- ✓
Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index
Why this is correct
Azure AI Language entity extraction as a custom skill can extract attributes like color and brand from product descriptions. Fuzzy search in the index handles misspelled queries.
Related concept
Entity extraction
- ✗
Use Azure AI Vision OCR to extract text, and enable synonyms
Why it's wrong here
Azure AI Vision OCR is used for extracting text from images, not for extracting entities from structured text. Synonyms may help with related terms but do not address fuzzy matching for misspellings.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Keyword trap
Azure AI Language key phrase extraction does not extract structured entities like color, size, or brand. Vector search is for semantic similarity, not fuzzy matching for misspellings.
Similar concept trap
Azure AI Language key phrase extraction does not extract structured entities like color, size, or brand. Vector search is for semantic similarity, not fuzzy matching for misspellings.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Entity extraction
- Fuzzy search
- Custom skill
- Azure AI Language
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Entity extraction
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. Entity extraction 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 entity extraction, then practise related AI-102 questions on the same topic to reinforce the concept.
- →
Implement knowledge mining and information extraction solutions — study guide chapter
Learn the concepts, then practise the questions
- →
Implement knowledge mining and information extraction solutions practice questions
Targeted practice on this topic area only
- →
All AI-102 questions
993 questions across all exam domains
- →
Microsoft Azure AI Engineer Associate AI-102 study guide
Full concept coverage aligned to exam objectives
- →
AI-102 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-102 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Implement an agentic solution practice questions
Practise AI-102 questions linked to Implement an agentic solution.
Implement computer vision solutions practice questions
Practise AI-102 questions linked to Implement computer vision solutions.
Implement knowledge mining and information extraction solutions practice questions
Practise AI-102 questions linked to Implement knowledge mining and information extraction solutions.
Implement image and video processing solutions practice questions
Practise AI-102 questions linked to Implement image and video processing solutions.
Implement natural language processing solutions practice questions
Practise AI-102 questions linked to Implement natural language processing solutions.
Implement generative AI solutions practice questions
Practise AI-102 questions linked to Implement generative AI solutions.
Implement agentic AI solutions practice questions
Practise AI-102 questions linked to Implement agentic AI solutions.
Implement knowledge mining and document intelligence solutions practice questions
Practise AI-102 questions linked to Implement knowledge mining and document intelligence solutions.
Plan and manage an Azure AI solution practice questions
Practise AI-102 questions linked to Plan and manage an Azure AI solution.
Implement content moderation solutions practice questions
Practise AI-102 questions linked to Implement content moderation solutions.
AI-102 fundamentals practice questions
Practise AI-102 questions linked to AI-102 fundamentals.
AI-102 scenario practice questions
Practise AI-102 questions linked to AI-102 scenario.
Practice this exam
Start a free AI-102 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Entity extraction.
What is the correct answer to this question?
The correct answer is: Use Azure AI Language entity extraction as a custom skill, and enable fuzzy search in the index — Option C is correct. Using Azure AI Language entity extraction as a custom skill extracts attributes like color, size, and brand from product descriptions, and enabling fuzzy search in the index handles misspellings. Option A is incorrect because key phrase extraction identifies topics, not specific entities like color or brand, and vector search is for similarity matching, not fuzzy matching for typos. Option B is incorrect because Azure AI Document Intelligence is designed to extract text from documents (e.g., PDFs, images), not to extract named entities from text already stored in a database; semantic ranking improves relevance but does not perform entity extraction. Option D is incorrect because Azure AI Vision OCR extracts text from images, not from product descriptions in Cosmos DB, and synonyms expand queries but do not extract entities needed for filtering.
What should I do if I get this AI-102 question wrong?
Review entity extraction, then practise related AI-102 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Entity extraction
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 →
Keep practising
More AI-102 practice questions
- Drag and drop the steps to set up Azure AI Content Safety for content moderation into the correct order.
- Drag and drop the steps to configure an Azure AI Search index with a custom skill into the correct order.
- You have an Azure AI Search index defined as shown in the exhibit. Users want to filter search results by author and by…
- Refer to the exhibit. You are configuring an Azure AI Video Indexer job. The exhibit shows a JSON snippet of the job con…
- Refer to the exhibit. You are configuring the Azure AI Vision Analyze Image API. You need to ensure that the response in…
- A company is deploying a generative AI solution using Azure OpenAI Service to generate product descriptions. The solutio…
Last reviewed: Jun 20, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.