The correct answer is to add both the Entity Recognition skill and the OCR skill to the Azure AI Search skillset. This is because the built-in Entity Recognition skill is specifically designed to identify people, organizations, and locations from text, but PDF documents stored in Azure Blob Storage often contain scanned images or non-selectable text. The OCR (Optical Character Recognition) skill must be applied first to extract the raw text from those PDFs, enabling the Entity Recognition skill to then analyze that text for the required entities. On the AI-102 exam, this question tests your understanding of skill composition and the dependency chain within an enrichment pipeline—a common trap is forgetting that OCR is a prerequisite for entity extraction on image-based PDFs. A useful memory tip is “OCR before Entity”: think of OCR as the eyes that read the document, and Entity Recognition as the brain that identifies who and what is mentioned.
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.
A company is building a knowledge mining solution using Azure AI Search. They need to extract entities from a large set of PDF documents stored in Azure Blob Storage. The solution must use a built-in AI skill to identify people, organizations, and locations. Which TWO actions should be taken? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable OCR (Optical Character Recognition) in the indexer configuration.
Option A is correct because the Entity Recognition skill identifies people, organizations, and locations. Option D is correct because the built-in OCR skill is needed to extract text from PDFs before entity recognition can occur. Options B, C, and E are either unrelated or unnecessary.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Configure the index to use a custom analyzer.
Why it's wrong here
Custom analyzers are for text analysis, not entity extraction.
✗
Add the Key Phrase Extraction skill to the skillset.
Why it's wrong here
Key Phrase Extraction extracts key phrases, not specific entity types.
✗
Deploy a custom skill using Azure Functions to extract entities.
Why it's wrong here
Built-in skills are available; a custom skill is not required.
✓
Enable OCR (Optical Character Recognition) in the indexer configuration.
Why this is correct
OCR extracts text from PDFs so that the Entity Recognition skill can process it.
Related concept
Read the scenario before looking for a memorised answer.
✓
Add the Entity Recognition skill to the skillset.
Why this is correct
The Entity Recognition skill extracts entities like people, organizations, and locations.
Related concept
Read the scenario before looking for a memorised answer.
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
Key Phrase Extraction extracts key phrases, not specific entity types.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable OCR (Optical Character Recognition) in the indexer configuration. — Option A is correct because the Entity Recognition skill identifies people, organizations, and locations. Option D is correct because the built-in OCR skill is needed to extract text from PDFs before entity recognition can occur. Options B, C, and E are either unrelated or unnecessary.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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 building a knowledge mining solution for legal documents using Azure AI Search. The solution must extract entities like dates, organizations, and persons from PDF files and index them. Which built-in skill should you add to the skillset to perform this extraction?
medium
✓ A.Named Entity Recognition skill
B.Language Detection skill
C.Optical Character Recognition (OCR) skill
D.Key Phrase Extraction skill
Why A: The Named Entity Recognition (NER) skill extracts entities like persons, organizations, and dates from text. Option A is incorrect because OCR is for extracting text from images, not entities. Option C is incorrect because Key Phrase Extraction extracts key phrases, not named entities. Option D is incorrect because Language Detection identifies language.
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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
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