- A
Speech to Text and Text Analytics API (standard)
Why wrong: The standard Text Analytics API can extract general entities like names and locations but does not have built-in medical entity recognition.
- B
Speech to Text and Text Analytics for Health
Speech to Text provides real-time transcription, and Text Analytics for Health is specifically designed to extract medical concepts from clinical text.
- C
Translator Text and Language Understanding (LUIS)
Why wrong: Translator Text translates between languages, and LUIS extracts intent and entities from utterances. Neither service transcribes audio nor specializes in medical extraction.
- D
Speaker Recognition and Question Answering
Why wrong: Speaker Recognition identifies who is speaking, and Question Answering provides answers from a knowledge base. They do not perform transcription or medical entity extraction.
Quick Answer
The answer is a combination of Azure Speech to Text and Text Analytics for Health. Speech to Text handles the real-time transcription of doctor-patient conversations, converting audio into text, while Text Analytics for Health then extracts medical entities such as conditions, medications, and dosages from that transcribed text using specialized clinical ontologies like UMLS and SNOMED CT. On the AI-900 exam, this scenario tests your understanding of how Azure services specialize for healthcare—a common trap is choosing the standard Text Analytics API, which only extracts general entities like names or locations, not clinical data. Remember the memory tip: “Speech for the words, Health for the herbs”—Speech to Text captures the spoken words, and Text Analytics for Health extracts the medical details.
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. 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 hospital wants to create a system that can transcribe doctor-patient conversations in real time and also extract medical conditions, medications, and dosages from the transcribed text. Which combination of Azure AI services 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
Speech to Text and Text Analytics for Health
Option B is correct because the scenario requires real-time transcription of doctor-patient conversations, which is handled by Azure Speech to Text, and then extraction of medical entities like conditions, medications, and dosages from the transcribed text, which is specifically provided by Azure Text Analytics for Health. Text Analytics for Health is a specialized container or API within Azure Cognitive Services that uses medical ontologies (e.g., UMLS, SNOMED CT) to extract clinical entities, unlike the standard Text Analytics API which only extracts general entities like names or locations.
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.
- ✗
Speech to Text and Text Analytics API (standard)
Why it's wrong here
The standard Text Analytics API can extract general entities like names and locations but does not have built-in medical entity recognition.
- ✓
Speech to Text and Text Analytics for Health
Why this is correct
Speech to Text provides real-time transcription, and Text Analytics for Health is specifically designed to extract medical concepts from clinical text.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Translator Text and Language Understanding (LUIS)
Why it's wrong here
Translator Text translates between languages, and LUIS extracts intent and entities from utterances. Neither service transcribes audio nor specializes in medical extraction.
- ✗
Speaker Recognition and Question Answering
Why it's wrong here
Speaker Recognition identifies who is speaking, and Question Answering provides answers from a knowledge base. They do not perform transcription or medical entity extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the standard Text Analytics API with Text Analytics for Health, assuming the general API can extract medical entities, but only the health-specific version has the clinical ontology and relation extraction capabilities required for this use case.
Detailed technical explanation
How to think about this question
Azure Speech to Text uses deep neural network models (e.g., CRDNN with self-attention) to convert audio streams into text in real time, supporting custom acoustic and language models for medical terminology. Text Analytics for Health leverages a pre-trained NLP model that maps extracted entities to standardized clinical codes like RxNorm for medications and ICD-10 for conditions, and can handle negation detection (e.g., 'no fever') and relation extraction (e.g., 'take 500 mg of ibuprofen'). In a real-world scenario, the system must handle overlapping speech and background noise, which Speech to Text can mitigate with diarization and custom models trained on clinical audio.
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: Speech to Text and Text Analytics for Health — Option B is correct because the scenario requires real-time transcription of doctor-patient conversations, which is handled by Azure Speech to Text, and then extraction of medical entities like conditions, medications, and dosages from the transcribed text, which is specifically provided by Azure Text Analytics for Health. Text Analytics for Health is a specialized container or API within Azure Cognitive Services that uses medical ontologies (e.g., UMLS, SNOMED CT) to extract clinical entities, unlike the standard Text Analytics API which only extracts general entities like names or locations.
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 →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?
hard- A.Language Understanding (LUIS)
- ✓ B.Text Analytics
- C.Translator Text
- D.Speech
Why B: Text Analytics (now part of Azure AI Language) is the correct service because it provides pre-built entity extraction capabilities specifically designed to identify and extract named entities like people (patient names), quantities (medication dosages), and dates from unstructured text. This aligns directly with the requirement to extract specific data elements from doctors' notes without needing custom model training.
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.
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