The answer is Named Entity Recognition (NER), the Azure AI Language feature designed to identify and categorize entities in unstructured text, including PII categories like names, dates, and social security numbers. NER’s pre-built PII detection model can automatically locate and redact these sensitive elements from medical notes, enabling the hospital to anonymize patient records efficiently. On the AI-900 exam, this scenario tests your understanding of Azure AI Language’s core capabilities—specifically how NER differs from other features like text classification or sentiment analysis. A common trap is confusing NER with key phrase extraction; remember that NER specifically tags known entity types (e.g., person, date, SSN) rather than just pulling out important terms. For a quick memory tip: think “NER tags the names and numbers” to recall that it handles structured PII removal from free-form clinical text.
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 automatically anonymize patient medical records by removing all personally identifiable information (PII) such as names, dates, and social security numbers from unstructured text notes. Which Azure AI Language feature should they use?
Why wrong: Key phrase extraction identifies important topics or phrases, but does not specifically extract entities like names or social security numbers.
B
Named entity recognition (NER)
NER detects and classifies entities (e.g., person, date, organization) from text, including PII. Azure's PII detection feature is built on NER, making it the right choice for this task.
C
Sentiment analysis
Why wrong: Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not suitable for identifying PII.
D
Language detection
Why wrong: Language detection identifies the language of the text (e.g., English, Spanish), not specific entities within the text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Named entity recognition (NER)
Named entity recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities in unstructured text, including PII categories such as names, dates, and social security numbers. The hospital can use NER's pre-built PII detection model to automatically locate and redact these sensitive elements from patient notes, meeting their anonymization requirement.
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 topics or phrases, but does not specifically extract entities like names or social security numbers.
✓
Named entity recognition (NER)
Why this is correct
NER detects and classifies entities (e.g., person, date, organization) from text, including PII. Azure's PII detection feature is built on NER, making it the right choice for this task.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not suitable for identifying PII.
✗
Language detection
Why it's wrong here
Language detection identifies the language of the text (e.g., English, Spanish), not specific entities within the text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with NER, thinking that extracting 'important phrases' includes names and dates, but key phrase extraction only returns topical phrases, not categorized PII entities.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important topics or phrases, but does not specifically extract entities like names or social security numbers.
Detailed technical explanation
How to think about this question
Azure's NER for PII uses a deep learning model trained on a large corpus of labeled data to recognize over 20 PII categories, including US social security numbers (pattern: ###-##-####), dates (e.g., '01/15/2023'), and person names. The service can return both the entity text and its offset position in the document, enabling precise redaction. In a real-world scenario, NER can also handle composite entities like 'Dr. Smith on 05/20/2024' by detecting multiple PII types in a single sentence.
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: Named entity recognition (NER) — Named entity recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities in unstructured text, including PII categories such as names, dates, and social security numbers. The hospital can use NER's pre-built PII detection model to automatically locate and redact these sensitive elements from patient notes, meeting their anonymization requirement.
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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