Question 550 of 988

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

Quick Answer

The answer is to use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy it as a custom skill in Azure AI Search. This approach is correct because Document Intelligence offers prebuilt handwriting OCR capabilities that can be fine-tuned with minimal labeled examples to extract specific fields like student name, ID, and degree, directly feeding into an AI Search index. On the AI-102 exam, this scenario tests your understanding of combining Document Intelligence’s custom extraction models with Azure AI Search’s enrichment pipeline, where the trap is confusing OCR-only extraction (which lacks field-level parsing) with Document Intelligence’s purpose-built form understanding. A common distractor is Custom Vision, but it is designed for image classification, not text extraction from forms. Memory tip: think “Doc Intelligence for fields, Search for indexing” — the custom skill bridges the two, turning scanned handwriting into searchable data with minimal labeling effort.

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 data engineer at a university. The university wants to digitize its historical student records (paper forms) to make them searchable. The records are scanned as images (JPEG) and stored in Azure Blob Storage. Each form contains handwritten fields: student name, ID number, date of birth, and degree. You need to extract these fields and index them in Azure AI Search. The solution must use Azure AI Services and minimize manual labeling effort. Which approach should you take?

Clue words in this question

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

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1easymultiple choice

Full question →

A
Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.
Why wrong: Requires manual labeling and two-step process; not optimal.
B
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.
Why wrong: Entity Recognition not reliable for handwritten fields without context.
C
Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.
Document Intelligence is designed for extraction from forms with minimal labeling.
D
Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.
Why wrong: Entity Recognition may not correctly identify fields without context; no structure.

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 Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.

Option B is correct because Azure AI Document Intelligence has prebuilt models for handwriting and can extract fields with minimal training. Option A requires custom training and labeling. Option C uses OCR but not extraction. Option D uses Custom Vision which is not suitable for text extraction.

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.

✗
Use Azure AI Custom Vision to train a model to detect handwriting regions, then use Azure AI Vision OCR to read text.
Why it's wrong here
Requires manual labeling and two-step process; not optimal.
✗
Use Azure AI Search with a blob indexer and a skillset that includes OCR skill and Entity Recognition skill.
Why it's wrong here
Entity Recognition not reliable for handwritten fields without context.
✓
Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search.
Why this is correct
Document Intelligence is designed for extraction from forms with minimal labeling.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Azure AI Vision OCR to extract text from images, then use Azure AI Language to extract entities like name, date, and degree.
Why it's wrong here
Entity Recognition may not correctly identify fields without context; no structure.

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.

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.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use Azure AI Document Intelligence to train a custom extraction model with a few labeled samples, then deploy as a custom skill in Azure AI Search. — Option B is correct because Azure AI Document Intelligence has prebuilt models for handwriting and can extract fields with minimal training. Option A requires custom training and labeling. Option C uses OCR but not extraction. Option D uses Custom Vision which is not suitable for text extraction.

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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

2 more ways this is tested on AI-102

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 designing a solution to extract structured data from a large number of handwritten forms. The forms are scanned and stored as images. Which Azure AI feature should you use?

easy

A.Azure AI Vision's image analysis
B.Azure AI Speech to text
C.Azure Bot Service
✓ D.Azure AI Document Intelligence's OCR capability

Why D: Option D is correct because the OCR skill in Azure AI Document Intelligence (or Azure AI Search) can extract text from handwritten images. Option A is for speech. Option B is for image analysis, not text extraction from forms. Option C is for conversational AI.

Variation 2. You are designing a knowledge mining solution that must extract entities from scanned handwritten forms. The forms contain signatures and checkboxes. Which combination of Azure AI services should you recommend?

hard

✓ A.Azure AI Document Intelligence with a custom neural model and Azure AI Language for entity linking
B.Azure AI Document Intelligence with a premade model and Azure AI Computer Vision
C.Azure AI Computer Vision (OCR) and Azure AI Search with integrated vectorization
D.Azure Cognitive Search and Azure AI Document Intelligence with a premade model

Why A: Option A is correct because Document Intelligence can extract handwriting and layout, and AI Language can post-process entities. Option B is wrong because Computer Vision OCR is for printed text only. Option C is wrong because Cognitive Search is not an extraction service. Option D is wrong because AI Document Intelligence already includes OCR; adding Computer Vision is redundant.

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