Quick Answer
The correct answer is Option C because enabling active learning in your custom NER project directly addresses the need to improve model accuracy without significantly increasing labeling effort. Active learning works by having the model identify the most uncertain predictions—such as borderline cases for 'Statute' or 'Case Citation'—and then prompting you to review and label only those high-impact examples, which efficiently boosts performance where the model struggles most. On the AI-102 exam, this scenario tests your understanding of how to optimize custom NER workflows in Azure AI Language, often appearing as a trap where you might instinctively choose manual labeling (Option A) or misapply AutoML (Option D). The key distinction is that active learning is purpose-built for iterative NER refinement, not classification. Memory tip: think of active learning as a "smart filter" that surfaces only the confusing cases, saving you from labeling thousands of obvious ones.
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 an AI developer at a legal firm. The firm uses Azure AI Language to extract entities from legal documents. The current custom NER model is trained on a small dataset and has low accuracy for certain entity types like 'Statute' and 'Case Citation'. You need to improve the model's performance without increasing the labeling effort significantly. You have the following options:
Option A: Add more labeled examples for the underperforming entity types by manually labeling additional documents.
Option B: Use the prebuilt entity recognition model from Azure AI Language and map its outputs to custom entities.
Option C: Enable active learning in the custom NER project and review the suggested labels from the model.
Option D: Train a new model using the Azure Machine Learning automated ML (AutoML) for text classification.
Option D
Why wrong: AutoML is for classification, not NER.
Option B
Why wrong: Prebuilt models may not extract legal-specific entities.
Option A
Why wrong: Increases labeling effort significantly.
Option C
Active learning focuses effort on uncertain predictions.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Option C
Option C is correct. Active learning suggests uncertain predictions for manual review, focusing effort on the most impactful examples. Option A is wrong because it increases labeling effort significantly. Option B is wrong because prebuilt models may not extract legal-specific entities. Option D is wrong because AutoML is for classification, not NER 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.
Option D
Why it's wrong here
AutoML is for classification, not NER.
Option B
Why it's wrong here
Prebuilt models may not extract legal-specific entities.
Option A
Why it's wrong here
Increases labeling effort significantly.
Option C
Why this is correct
Active learning focuses effort on uncertain predictions.
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: Option C — Option C is correct. Active learning suggests uncertain predictions for manual review, focusing effort on the most impactful examples. Option A is wrong because it increases labeling effort significantly. Option B is wrong because prebuilt models may not extract legal-specific entities. Option D is wrong because AutoML is for classification, not NER 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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