AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. 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.
A legal firm needs to automatically extract case-specific entities such as 'docket number', 'plaintiff attorney', and 'court name' from legal documents. They have a small set of manually labeled examples for each entity. Which Azure AI Language feature should they use to build this custom entity extraction solution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom named entity recognition (NER)
Custom named entity recognition (NER) allows you to train a model with your own labeled examples to extract domain-specific entities like 'docket number' and 'plaintiff attorney'. Prebuilt entity extraction only recognizes common, generic entities (e.g., person, location) and cannot be customized for legal case-specific terms. This makes custom NER the correct choice for building a tailored extraction solution with a small set of manually labeled data.
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.
✓
Custom named entity recognition (NER)
Why this is correct
Custom NER enables training on labeled examples to extract tailored entities relevant to the legal domain.
Related concept
Read the scenario before looking for a memorised answer.
✗
Prebuilt entity extraction
Why it's wrong here
Prebuilt extraction recognizes generic entity types and cannot be customized to extract specialized legal entities like 'docket number'.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases but does not map them to predefined entity categories.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates the emotional tone of text and does not extract specific entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'prebuilt entity extraction' (which is fixed and generic) with 'custom named entity recognition' (which is trainable), assuming that prebuilt models can be adapted to domain-specific entities without additional training.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but does not map them to predefined entity categories.
Detailed technical explanation
How to think about this question
Custom NER in Azure AI Language uses a transformer-based model fine-tuned on your labeled dataset via a training pipeline that splits data into training, validation, and test sets. Under the hood, it employs a conditional random field (CRF) layer on top of a BERT-style encoder to predict entity spans with high precision. In a real-world scenario, a legal firm might label only 50–100 documents per entity type and still achieve strong extraction accuracy, as the model leverages transfer learning from the base language model.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Custom named entity recognition (NER) — Custom named entity recognition (NER) allows you to train a model with your own labeled examples to extract domain-specific entities like 'docket number' and 'plaintiff attorney'. Prebuilt entity extraction only recognizes common, generic entities (e.g., person, location) and cannot be customized for legal case-specific terms. This makes custom NER the correct choice for building a tailored extraction solution with a small set of manually labeled data.
What should I do if I get this AI-900 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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