- A
NER that works across spans (paragraphs) of text rather than single sentences
Why wrong: Cross-sentence NER is a document-level concern — span-based NER identifies entity spans within text as units for classification.
- B
Identifying text spans as candidate entities and classifying each span — handling overlapping and nested entities
Span-based NER extracts and classifies spans directly — naturally handling overlapping entities that sequence labelling struggles with.
- C
A technique that spans multiple languages to recognise entities in multilingual text
Why wrong: Cross-lingual NER is multilingual capability — span-based is an architectural approach to entity detection within a single text.
- D
NER that spans multiple documents to track entities across a corpus
Why wrong: Cross-document entity tracking is coreference resolution — span-based NER identifies entities within a single document.
Quick Answer
The correct answer is that span-based named entity recognition identifies text spans as candidate entities and classifies each span, enabling it to handle overlapping and nested entities. Unlike sequence labeling, which assigns a single label per token using methods like BIO tagging, span-based NER first locates contiguous token sequences and then classifies them, allowing it to recognize both "University of Washington" as an organization and "Washington" as a location within the same sentence—a task sequence labeling struggles with due to its flat, token-level approach. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how Azure AI Language’s custom NER works, often appearing in questions about entity extraction capabilities. A common trap is assuming all NER uses sequence labeling; remember that span-based methods excel with hierarchical or overlapping entities. Memory tip: think "spans for stacks"—spans can stack nested entities, while sequence labeling flattens them.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'span-based named entity recognition' and how does it differ from sequence labelling?
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
Identifying text spans as candidate entities and classifying each span — handling overlapping and nested entities
Span-based named entity recognition (NER) identifies candidate entities by first locating text spans (contiguous sequences of tokens) and then classifying each span into an entity type. This differs from sequence labeling (e.g., BIO tagging) because it can naturally handle overlapping and nested entities—for example, recognizing both "University of Washington" as an organization and "Washington" as a location within the same sentence—whereas sequence labeling typically assigns a single label per token and struggles with such hierarchies.
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.
- ✗
NER that works across spans (paragraphs) of text rather than single sentences
Why it's wrong here
Cross-sentence NER is a document-level concern — span-based NER identifies entity spans within text as units for classification.
- ✓
Identifying text spans as candidate entities and classifying each span — handling overlapping and nested entities
Why this is correct
Span-based NER extracts and classifies spans directly — naturally handling overlapping entities that sequence labelling struggles with.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A technique that spans multiple languages to recognise entities in multilingual text
Why it's wrong here
Cross-lingual NER is multilingual capability — span-based is an architectural approach to entity detection within a single text.
- ✗
NER that spans multiple documents to track entities across a corpus
Why it's wrong here
Cross-document entity tracking is coreference resolution — span-based NER identifies entities within a single document.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'span' with 'document span' or 'paragraph span' (options A and D) or think it refers to multilingual coverage (option C), when the actual technical distinction is about handling overlapping and nested entities within a single text segment.
Detailed technical explanation
How to think about this question
Under the hood, span-based NER models often use a transformer encoder to produce token embeddings, then enumerate all possible spans up to a maximum length, compute span representations (e.g., via pooling or attention), and classify each span into entity types or 'non-entity'. A subtle behavior is that these models can predict overlapping entities of different types (e.g., a person name inside an organization name) by treating each span independently, whereas sequence labeling with BIO tags would require special tagging schemes (e.g., BILOU) that still cannot represent arbitrary nesting without additional mechanisms. In real-world scenarios like biomedical text mining, span-based NER is critical for extracting nested entities such as "EGFR gene mutation" where "EGFR" is a gene and the whole phrase is a mutation.
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-900 question test?
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: Identifying text spans as candidate entities and classifying each span — handling overlapping and nested entities — Span-based named entity recognition (NER) identifies candidate entities by first locating text spans (contiguous sequences of tokens) and then classifying each span into an entity type. This differs from sequence labeling (e.g., BIO tagging) because it can naturally handle overlapping and nested entities—for example, recognizing both "University of Washington" as an organization and "Washington" as a location within the same sentence—whereas sequence labeling typically assigns a single label per token and struggles with such hierarchies.
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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Last reviewed: Jun 11, 2026
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