- A
Key Phrase Extraction
Why wrong: Key Phrase Extraction identifies important terms in text but does not have a specialized medical model; it yields general phrases, not structured medical entities.
- B
Named Entity Recognition (NER)
Why wrong: Standard NER recognizes general entities like people, organizations, and locations, but it is not trained to extract medical-specific entities like diseases or medications.
- C
Text Analytics for Health
Text Analytics for Health is a prebuilt service specialized in extracting medical information from clinical texts, such as diseases, symptoms, medications, and dosages.
- D
Custom Text Classification
Why wrong: Custom Text Classification would require the user to provide labeled training data and train a model, which is not a prebuilt capability aimed at medical entities.
Quick Answer
The correct answer is Text Analytics for Health, a prebuilt Azure AI Language feature specifically trained on medical domain data to extract entities like diseases, symptoms, medications, and dosages from clinical trial documents. Unlike general entity extraction, Text Analytics for Health uses specialized medical ontologies and natural language processing models that understand clinical terminology, dosage units, and symptom relationships without requiring custom training. On the AI-900 exam, this question tests your ability to distinguish between prebuilt domain-specific capabilities and general-purpose features like standard Named Entity Recognition—a common trap is choosing “Custom Text Classification” or “Key Phrase Extraction,” which lack medical training. Remember the memory tip: “Health for healthcare” means if the task involves medical text, always look for the word “Health” in the Azure AI Language service name.
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 medical research organization needs to process thousands of clinical trial documents to automatically extract specific medical entities such as diseases, symptoms, medications, and dosages. They want to use a prebuilt Azure AI Language capability that is already trained on medical domain data. Which Azure AI Language feature should they use?
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
Text Analytics for Health
Text Analytics for Health is a prebuilt Azure AI Language capability specifically trained on medical domain data, enabling extraction of entities like diseases, symptoms, medications, and dosages from clinical trial documents without requiring custom model training. It is designed for healthcare and life sciences use cases, making it the correct choice for processing thousands of clinical documents automatically.
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.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction identifies important terms in text but does not have a specialized medical model; it yields general phrases, not structured medical entities.
- ✗
Named Entity Recognition (NER)
Why it's wrong here
Standard NER recognizes general entities like people, organizations, and locations, but it is not trained to extract medical-specific entities like diseases or medications.
- ✓
Text Analytics for Health
Why this is correct
Text Analytics for Health is a prebuilt service specialized in extracting medical information from clinical texts, such as diseases, symptoms, medications, and dosages.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Custom Text Classification
Why it's wrong here
Custom Text Classification would require the user to provide labeled training data and train a model, which is not a prebuilt capability aimed at medical entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse general-purpose Named Entity Recognition (NER) with domain-specific medical NER, assuming any NER can extract medical entities, but Text Analytics for Health is the only prebuilt Azure service trained on medical data for this purpose.
Trap categories for this question
Keyword trap
Key Phrase Extraction identifies important terms in text but does not have a specialized medical model; it yields general phrases, not structured medical entities.
Detailed technical explanation
How to think about this question
Text Analytics for Health leverages a specialized NLP model pre-trained on large biomedical corpora (e.g., PubMed, UMLS) and supports entity linking to standardized ontologies like SNOMED CT and RxNorm. It can handle complex medical concepts such as medication dosages with units (e.g., '50 mg') and negated symptoms (e.g., 'no fever'), which generic NER cannot reliably parse. In real-world scenarios, this feature is used for clinical trial data mining and EHR analysis, where accuracy on domain-specific entities is critical.
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.
- →
Describe features of Natural Language Processing workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of Natural Language Processing workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Text Analytics for Health — Text Analytics for Health is a prebuilt Azure AI Language capability specifically trained on medical domain data, enabling extraction of entities like diseases, symptoms, medications, and dosages from clinical trial documents without requiring custom model training. It is designed for healthcare and life sciences use cases, making it the correct choice for processing thousands of clinical documents automatically.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.