- A
Use the prebuilt NER model and map the recognized entities to the required types.
Why wrong: Prebuilt NER does not recognize specialized entities like gene and protein names.
- B
Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
Custom NER allows training a model tailored to the specific entities using the labeled data.
- C
Use Azure Logic Apps to call the Text Analytics API and post-process the results.
Why wrong: Logic Apps is an integration service and does not solve the entity extraction problem.
- D
Train a custom NER model for genes and use prebuilt NER for chemicals.
Why wrong: This approach duplicates effort; a single custom NER model can handle all entity types.
Question 241 of 993
AI-102 Practice Question: Implement natural language processing solutions
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.
A research organization uses Azure AI Language to process large volumes of scientific papers. They need to extract specific entities such as gene names, protein names, and chemical compounds. The entity types are highly specialized and not covered by prebuilt models. The organization has a labeled dataset of 10,000 documents. You need to recommend the most efficient approach to build the entity extraction solution. What should you do?
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
✓
Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
Option B is correct because Custom Named Entity Recognition (NER) in Azure AI Language allows you to train a model on your own labeled dataset (10,000 documents) to extract highly specialized entity types like gene names, protein names, and chemical compounds that are not covered by prebuilt models. This approach is the most efficient as it leverages the labeled data directly, avoiding the need for complex post-processing or hybrid solutions.
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 the prebuilt NER model and map the recognized entities to the required types.
Why it's wrong here
Prebuilt NER does not recognize specialized entities like gene and protein names.
- ✓
Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language.
Why this is correct
Custom NER allows training a model tailored to the specific entities using the labeled data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Azure Logic Apps to call the Text Analytics API and post-process the results.
Why it's wrong here
Logic Apps is an integration service and does not solve the entity extraction problem.
- ✗
Train a custom NER model for genes and use prebuilt NER for chemicals.
Why it's wrong here
This approach duplicates effort; a single custom NER model can handle all entity types.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume prebuilt NER can be adapted via mapping or post-processing, but Azure AI Language's prebuilt models are fixed and cannot recognize custom entity types without training a custom model.
Detailed technical explanation
How to think about this question
Custom NER in Azure AI Language uses a transformer-based model (e.g., BERT) fine-tuned on your labeled dataset, where each entity type is defined with a schema and tags. The service supports up to 100,000 documents for training, and the 10,000 documents provided are well within the recommended range for good performance. Under the hood, the model learns contextual embeddings specific to the scientific domain, enabling it to distinguish between, for example, a gene name like 'BRCA1' and a chemical compound like 'cisplatin' even in ambiguous contexts.
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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Train a Custom Named Entity Recognition (NER) model using the labeled dataset in Azure AI Language. — Option B is correct because Custom Named Entity Recognition (NER) in Azure AI Language allows you to train a model on your own labeled dataset (10,000 documents) to extract highly specialized entity types like gene names, protein names, and chemical compounds that are not covered by prebuilt models. This approach is the most efficient as it leverages the labeled data directly, avoiding the need for complex post-processing or hybrid solutions.
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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Last reviewed: Jul 4, 2026
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