The answer is a set of labeled documents for training, a deployed endpoint for the custom NER model, and an Azure Storage account with blob containers for your project data. These three components are required because the custom NER feature in Azure Cognitive Service for Language relies on supervised learning: labeled documents define the entity spans and types the model must learn, the storage account holds the training data and project artifacts, and the deployed endpoint allows you to consume the trained model via API calls. On the AI-102 exam, this question tests your understanding of the end-to-end workflow for building a custom NER model, often appearing as a multi-select scenario where distractors include unnecessary items like pre-built entity recognizers or Azure Functions. A common trap is forgetting the storage account, as candidates assume labeled data alone suffices. Memory tip: think "Data, Store, Serve" — labeled data in a storage account, then serve via an endpoint.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Which THREE components are required to build a custom named entity recognition (NER) model in Azure Cognitive Service for Language?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A set of labeled documents for training.
Option C is correct because a custom NER model in Azure Cognitive Service for Language requires a set of labeled documents for training. These labeled documents define the entities and their spans within text, which the model uses to learn patterns for extraction. Without labeled data, the model cannot be trained to recognize custom entities.
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.
✗
A QnA Maker knowledge base for entity definitions.
Why it's wrong here
QnA Maker is for question answering, not NER.
✗
A LUIS application to handle entity extraction.
Why it's wrong here
LUIS is a separate service for conversational NLU.
✓
A set of labeled documents for training.
Why this is correct
Labeled data is essential for training.
Related concept
Read the scenario before looking for a memorised answer.
✓
A language service project with a custom NER schema.
Why this is correct
The project defines entity types and tags.
Related concept
Read the scenario before looking for a memorised answer.
✓
A deployed endpoint for the custom NER model.
Why this is correct
The model must be deployed for inferencing.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the separate Azure AI services (QnA Maker, LUIS, Language service) and assume they are interchangeable for custom NER, when in fact each has a distinct role and pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Azure Cognitive Service for Language custom NER uses a transformer-based model that is fine-tuned on your labeled dataset. The labeled documents must include entity annotations with start and end offsets, and the service supports both flat and hierarchical entity schemas. In a real-world scenario, you might label thousands of documents to extract entities like 'product name' or 'defect code' from technical support logs, where the model learns context-specific patterns that generic NER cannot capture.
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.
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
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 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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A set of labeled documents for training. — Option C is correct because a custom NER model in Azure Cognitive Service for Language requires a set of labeled documents for training. These labeled documents define the entities and their spans within text, which the model uses to learn patterns for extraction. Without labeled data, the model cannot be trained to recognize custom entities.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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