- A
Verifying that a user's identity matches their stated credentials during login
Why wrong: Identity verification is authentication — PII detection finds and optionally redacts sensitive personal information in text.
- B
Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text
PII detection scans text for personal data and can redact it — supporting GDPR compliance and data anonymisation workflows.
- C
Tracking which users have accessed personally sensitive Azure resources
Why wrong: Access tracking is audit logging — PII detection is an NLP capability for finding personal information in text content.
- D
Detecting when users are sharing their own personal information in an inappropriate context
Why wrong: Inappropriate disclosure detection is a policy enforcement concern — PII detection is the technical capability of finding personal information in any 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.
What is 'personally identifiable information' (PII) detection in Azure AI Language?
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
Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text
Option B is correct because PII detection in Azure AI Language is a pre-built feature that identifies sensitive personal data such as names, social security numbers, email addresses, and phone numbers within unstructured text. It can also redact (mask) these entities to help comply with data privacy regulations like GDPR. This is a core capability of the Azure AI Language service's Text Analytics API.
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.
- ✗
Verifying that a user's identity matches their stated credentials during login
Why it's wrong here
Identity verification is authentication — PII detection finds and optionally redacts sensitive personal information in text.
- ✓
Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text
Why this is correct
PII detection scans text for personal data and can redact it — supporting GDPR compliance and data anonymisation workflows.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Tracking which users have accessed personally sensitive Azure resources
Why it's wrong here
Access tracking is audit logging — PII detection is an NLP capability for finding personal information in text content.
- ✗
Detecting when users are sharing their own personal information in an inappropriate context
Why it's wrong here
Inappropriate disclosure detection is a policy enforcement concern — PII detection is the technical capability of finding personal information in any text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse PII detection (identifying sensitive data in text) with authentication (verifying user identity) or access auditing (tracking resource access), leading them to pick options A or C.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's PII detection uses pre-trained machine learning models and regex-based pattern matching to identify over 60 entity types (e.g., US SSN, EU passport numbers, phone numbers). The service can return both the detected entities and their offsets in the text, and supports redaction by replacing each entity with a placeholder like '********'. A real-world scenario is a healthcare chatbot that automatically redacts patient names and medical record numbers from conversation logs before storing them for analysis.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text — Option B is correct because PII detection in Azure AI Language is a pre-built feature that identifies sensitive personal data such as names, social security numbers, email addresses, and phone numbers within unstructured text. It can also redact (mask) these entities to help comply with data privacy regulations like GDPR. This is a core capability of the Azure AI Language service's Text Analytics API.
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 →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.