- A
Option D
Why wrong: Option A (manual labeling) would increase labeling effort significantly and is not the most efficient approach; active learning is preferred.
- B
Option B
Why wrong: Option B is incorrect because prebuilt models cannot be directly mapped to custom entities; they are designed for general entity types.
- C
Option A
Option C is correct because active learning strategically selects documents that will most improve the model, reducing effort while targeting weak entity types.
- D
Option C
Why wrong: Option D is a distractor; AutoML for text classification is for text classification tasks, not NER, and does not leverage existing custom NER project.
Question 823 of 993
AI-102 Active learning Practice Question
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. A key principle to apply: active learning. 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.
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
✓
Option A
Option C is correct because active learning in Azure AI Language custom NER automatically identifies the most informative unlabeled documents and suggests labels for them, which directly targets the underperforming entity types ('Statute' and 'Case Citation') without requiring manual labeling of the entire dataset. This reduces labeling effort significantly while improving model accuracy by focusing on ambiguous or high-uncertainty examples.
Key principle: Active learning
Answer analysis
Option-by-option breakdown
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
Option A (manual labeling) would increase labeling effort significantly and is not the most efficient approach; active learning is preferred.
- ✗
Option B
Why it's wrong here
Option B is incorrect because prebuilt models cannot be directly mapped to custom entities; they are designed for general entity types.
- ✓
Option A
Why this is correct
Option C is correct because active learning strategically selects documents that will most improve the model, reducing effort while targeting weak entity types.
Related concept
Active learning
- ✗
Option C
Why it's wrong here
Option D is a distractor; AutoML for text classification is for text classification tasks, not NER, and does not leverage existing custom NER project.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse active learning (which reduces labeling effort by suggesting labels) with manual labeling (Option A) or assume prebuilt models can be adapted to custom entities (Option B), while AutoML (Option D) is a distractor for a different NLP task (classification vs. NER).
Detailed technical explanation
How to think about this question
Active learning in Azure AI Language custom NER works by training a model on the current labeled data, then scoring unlabeled documents based on prediction uncertainty (e.g., entropy or margin sampling). The system returns the top-k most uncertain documents for human review, which are the most likely to improve model performance when labeled. This iterative process is particularly effective for rare or complex entity types like legal citations, where the model's confidence is low, and it minimizes the total labeling effort by focusing only on high-value examples.
KKey Concepts to Remember
- Active learning
- Custom NER
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
Active learning
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. Active learning 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 active learning, then practise related AI-102 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Active learning.
What is the correct answer to this question?
The correct answer is: Option A — Option C is correct because active learning in Azure AI Language custom NER automatically identifies the most informative unlabeled documents and suggests labels for them, which directly targets the underperforming entity types ('Statute' and 'Case Citation') without requiring manual labeling of the entire dataset. This reduces labeling effort significantly while improving model accuracy by focusing on ambiguous or high-uncertainty examples.
What should I do if I get this AI-102 question wrong?
Review active learning, then practise related AI-102 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Active learning
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Last reviewed: Jul 4, 2026
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