A hospital receives patient notes in free text. They need to automatically identify entities like disease names, medications, and dosages from these notes without requiring any custom training. Which Azure AI Language feature is specifically designed for this medical entity extraction task?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
Best answer
Text Analytics for Health
This is correct. It is a prebuilt NLP model specialized for extracting medical entities from unstructured clinical text.
Distractor review
Custom Named Entity Recognition
Custom NER requires users to train a model with labeled data, which the scenario explicitly says they do not want to do.
Distractor review
Key Phrase Extraction
Key Phrase Extraction identifies general important phrases, not domain-specific medical entities.
Distractor review
Sentiment Analysis
Sentiment analysis determines the emotional tone of text, not extracting medical entities.
Common exam trap
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Technical deep dive
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
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Question 2
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Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
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 feature trained on a large corpus of medical data. It can extract entities like diseases, symptoms, medications, and dosages without the need for custom model training. Custom Named Entity Recognition would require labeled data to train a model. Key Phrase Extraction returns general key phrases, not medical-specific entities. Sentiment Analysis detects sentiment, not entities.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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