Quick Answer
The answer is Custom Entity Extraction, as it is the only Azure AI Language feature designed to handle custom entity extraction with small labeled data. This service uses a few-shot learning approach, enabling you to train a model on as few as ten to twenty labeled examples to identify specific entities like product names or order numbers, then continuously improve through iterative retraining. On the AI-102 exam, this scenario tests your understanding of when to choose custom over pre-built capabilities—a common trap is selecting Pre-built Entity Extraction, which fails because it cannot learn new entity types. Remember that custom extraction is part of the Azure AI Language custom text classification and extraction family, distinct from Text Analytics for health or QnA Maker. A helpful memory tip: think “custom for custom data, pre-built for common types”—if your entities are unique to your business, custom extraction is the path.
This AI-102 practice question tests your understanding of implement natural language processing 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 designing a solution that must extract specific entities from customer emails, such as product names, order numbers, and dates. The solution must be able to learn from a small set of labeled examples and improve over time. Which Azure AI service should you use?
QnA Maker
Why wrong: For question answering, not entity extraction.
Pre-built Entity Extraction
Why wrong: Cannot be trained on custom entities; limited to predefined types.
Text Analytics for health
Why wrong: Designed for medical entities, not general customer emails.
Custom Entity Extraction
Trainable with small labeled datasets to extract custom entities.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Custom Entity Extraction
Option C is correct because Custom Entity Extraction in Azure AI Language allows you to train a model with a small set of labeled data to extract custom entities. Option A is wrong because Pre-built Entity Extraction only works with predefined entity types. Option B is wrong because Text Analytics for health is specialized for medical data. Option D is wrong because the QnA Maker is for question answering, not entity extraction.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
QnA Maker
Why it's wrong here
For question answering, not entity extraction.
Pre-built Entity Extraction
Why it's wrong here
Cannot be trained on custom entities; limited to predefined types.
Text Analytics for health
Why it's wrong here
Designed for medical entities, not general customer emails.
Custom Entity Extraction
Why this is correct
Trainable with small labeled datasets to extract custom entities.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
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
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Custom Entity Extraction — Option C is correct because Custom Entity Extraction in Azure AI Language allows you to train a model with a small set of labeled data to extract custom entities. Option A is wrong because Pre-built Entity Extraction only works with predefined entity types. Option B is wrong because Text Analytics for health is specialized for medical data. Option D is wrong because the QnA Maker is for question answering, not entity extraction.
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.
Read the scenario before looking for a memorised answer.
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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.
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