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 hospital wants to automatically extract patient symptoms and medication names from clinical notes. They have a set of pre-defined categories for symptoms and medications, and they have manually labeled a few hundred sentences to indicate which text spans belong to each category. Which Azure AI Language feature should they use to build this custom entity extraction solution?
Why wrong: Pre-built entity recognition can extract common entities (e.g., person, date) but cannot be customized for specific medical categories like symptoms.
B
Key phrase extraction
Why wrong: Key phrase extraction identifies important phrases but does not classify them into custom categories or extract spans.
C
Custom text classification
Why wrong: Custom text classification assigns a category to the entire document, not extracting specific text spans for entities.
D
Custom entity extraction
Custom entity extraction allows you to train a model using labeled examples to extract specific custom entities (e.g., 'symptom', 'medication') from text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom entity extraction
Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.
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.
✗
Pre-built entity recognition
Why it's wrong here
Pre-built entity recognition can extract common entities (e.g., person, date) but cannot be customized for specific medical categories like symptoms.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases but does not classify them into custom categories or extract spans.
✗
Custom text classification
Why it's wrong here
Custom text classification assigns a category to the entire document, not extracting specific text spans for entities.
✓
Custom entity extraction
Why this is correct
Custom entity extraction allows you to train a model using labeled examples to extract specific custom entities (e.g., 'symptom', 'medication') from text.
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 confuse 'custom text classification' (which labels whole documents) with 'custom entity extraction' (which labels specific spans), leading them to pick option C when the question explicitly asks for extracting text spans, not classifying entire sentences.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but does not classify them into custom categories or extract spans.
Detailed technical explanation
How to think about this question
Under the hood, custom entity extraction in Azure AI Language uses a transformer-based model that you fine-tune with your labeled data. The model learns to identify entity boundaries (start and end positions) and assign them to your custom categories. A subtle behavior is that the model can handle overlapping entities and nested spans, which is crucial for clinical notes where a symptom might be described with multiple words or include negations.
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 entity extraction — Custom entity extraction (D) is the correct choice because the hospital needs to identify specific text spans (symptoms and medication names) based on their own pre-defined categories, using a small set of manually labeled sentences for training. This is exactly what Azure's custom named entity recognition (NER) feature does—it allows you to train a model to extract custom entities from unstructured text, tailored to your domain-specific labels.
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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