- A
PII detection and Text Analytics for Health
PII detection finds personal data to redact; Text Analytics for Health extracts medical concepts, preserving clinical information. Both are prebuilt and require no custom training.
- B
Key phrase extraction and sentiment analysis
Why wrong: Key phrase extraction finds important terms but does not distinguish between clinical and personal data. Sentiment analysis detects positive/negative tone, which is irrelevant for redaction.
- C
Named Entity Recognition (NER) and language detection
Why wrong: Standard NER can identify some entities (e.g., person names) but is not specialized for medical terms. Language detection identifies the language but offers no redaction or medical extraction.
- D
Entity linking and conversational language understanding
Why wrong: Entity linking maps entities to knowledge base concepts, not designed for redaction. Conversational language understanding is for chatbots, not medical text analysis.
Quick Answer
The correct combination is PII detection and Text Analytics for Health, because PII detection automatically identifies and redacts sensitive patient data like names and addresses, while Text Analytics for Health extracts clinical terms such as disease names and medications—both are prebuilt Azure AI Language features that require no custom training. On the AI-900 exam, this scenario tests your understanding of how to pair specialized preconfigured services to solve a real-world healthcare compliance need without building custom models. A common trap is choosing a single feature like Text Analytics for Health alone, which handles clinical entities but does not redact PII, or opting for custom NER, which violates the “no custom training” constraint. Remember the memory tip: “Redact the person, keep the patient”—PII detection handles the personal identifiers, while Text Analytics for Health preserves the clinical context.
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 healthcare organization needs to automatically redact personally identifiable information (PII) such as patient names and addresses from unstructured medical notes, while keeping clinical terms like disease names and medications. They want to use prebuilt Azure AI Language features without any custom training. Which combination of features 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
PII detection and Text Analytics for Health
Option A is correct because PII detection identifies and redacts personally identifiable information like patient names and addresses, while Text Analytics for Health extracts clinical entities such as diseases and medications from unstructured medical notes. Both are prebuilt Azure AI Language features that require no custom training, making them ideal for this healthcare redaction scenario.
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.
- ✓
PII detection and Text Analytics for Health
Why this is correct
PII detection finds personal data to redact; Text Analytics for Health extracts medical concepts, preserving clinical information. Both are prebuilt and require no custom training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Key phrase extraction and sentiment analysis
Why it's wrong here
Key phrase extraction finds important terms but does not distinguish between clinical and personal data. Sentiment analysis detects positive/negative tone, which is irrelevant for redaction.
- ✗
Named Entity Recognition (NER) and language detection
Why it's wrong here
Standard NER can identify some entities (e.g., person names) but is not specialized for medical terms. Language detection identifies the language but offers no redaction or medical extraction.
- ✗
Entity linking and conversational language understanding
Why it's wrong here
Entity linking maps entities to knowledge base concepts, not designed for redaction. Conversational language understanding is for chatbots, not medical text analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse general-purpose Named Entity Recognition (NER) with the specialized Text Analytics for Health feature, or assume that PII detection alone can handle clinical terms, when in fact two separate prebuilt features are needed for this specific healthcare redaction task.
Trap categories for this question
Keyword trap
Key phrase extraction finds important terms but does not distinguish between clinical and personal data. Sentiment analysis detects positive/negative tone, which is irrelevant for redaction.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's PII detection uses a pre-trained model that recognizes categories like Person, Address, and PhoneNumber, and can be configured to redact detected entities by replacing them with placeholders. Text Analytics for Health leverages a specialized biomedical model trained on medical literature and clinical notes to extract entities such as DiagnosisName, MedicationName, and Symptom, and also supports relation extraction (e.g., medication dosage). In a real-world scenario, a hospital could process thousands of discharge summaries daily, using PII detection to mask patient identifiers before sharing notes for research, while Text Analytics for Health extracts clinical terms for analytics.
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: PII detection and Text Analytics for Health — Option A is correct because PII detection identifies and redacts personally identifiable information like patient names and addresses, while Text Analytics for Health extracts clinical entities such as diseases and medications from unstructured medical notes. Both are prebuilt Azure AI Language features that require no custom training, making them ideal for this healthcare redaction scenario.
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
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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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