Describe features of Natural Language Processing workloads on Azure →hardMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?
⚠ Common exam trap
Watch out — candidates often confuse general NLP features (like Key Phrase Extraction) with the specialized Q&A service, not realizing that Custom Question Answering is the only option that directly supports building a knowledge base from documents for a bot.
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
✓
Custom Question Answering
Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis scores text on a positive-to-negative scale using pre-trained models, often to gauge employee feedback or opinion. It has no notion of fact retrieval or question intent, and it cannot return an answer from a document corpus. Using it in a bot would produce only a mood label, not a useful response to a question like 'What is our parental leave policy?'.
When this WOULD be correct
A company wants to analyze customer reviews to determine overall satisfaction with a product. Sentiment Analysis would be the correct feature to classify each review's sentiment.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction is an Azure Text Analytics capability that identifies the most important nouns and verb phrases in a document to support summarization or content categorization. It does not maintain a knowledge base or interpret a user's question, so it cannot retrieve a specific answer from an HR document. Even if you executed it on a query, it would only output keywords, not an explanatory response.
When this WOULD be correct
An exam question asks: 'A company wants to automatically extract the most important topics from thousands of customer reviews to identify common issues. Which Azure AI Language feature should they use?'
- ✓
Custom Question Answering
Why this is correct
Custom Question Answering (formerly QnA Maker) is a purpose-built Azure AI Language feature that ingests HR policy documents, PDFs, and FAQ pages to create a knowledge base. It uses semantic ranking and confidence scoring to match a user's natural-language query to the most relevant passage, then returns that passage as an answer. This directly satisfies the self-service bot requirement because the service is explicitly designed to answer questions from provided content.
- ✗
Language Detection
Why it's wrong here
Language Detection identifies the written language of a document by analyzing character patterns and common words, returning codes such as 'en' or 'fr'. It does not reason about content or extract answers, and it lacks any memory of the company's HR information. It might be a preprocessing step for a bot, but it cannot supply the substantive answer the self-service bot needs.
When this WOULD be correct
A company receives customer feedback in multiple languages and needs to route each message to the appropriate language-specific support team. Language Detection would be used to identify the language of each message.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Custom Question AnsweringCorrect answer▾
Why this is correct
Custom Question Answering (formerly QnA Maker) is a purpose-built Azure AI Language feature that ingests HR policy documents, PDFs, and FAQ pages to create a knowledge base. It uses semantic ranking and confidence scoring to match a user's natural-language query to the most relevant passage, then returns that passage as an answer. This directly satisfies the self-service bot requirement because the service is explicitly designed to answer questions from provided content.
✗Sentiment AnalysisWrong answer — click to see why▾
Why this is wrong here
Sentiment Analysis detects positive, negative, or neutral sentiment in text, but it cannot ingest PDF documents or answer questions based on policy content.
★ When this WOULD be the correct answer
A company wants to analyze customer reviews to determine overall satisfaction with a product. Sentiment Analysis would be the correct feature to classify each review's sentiment.
Why candidates choose this
Candidates may confuse sentiment analysis with understanding document content, thinking it can extract meaning from policy documents to answer questions.
✗Key Phrase ExtractionWrong answer — click to see why▾
Why this is wrong here
Key Phrase Extraction identifies important terms in text but does not enable a question-answering system over documents; it lacks the ability to map questions to answers from ingested PDFs.
★ When this WOULD be the correct answer
An exam question asks: 'A company wants to automatically extract the most important topics from thousands of customer reviews to identify common issues. Which Azure AI Language feature should they use?'
Why candidates choose this
Candidates may think extracting key phrases from policy documents is sufficient to answer questions, confusing information retrieval with question answering.
✗Language DetectionWrong answer — click to see why▾
Why this is wrong here
Language Detection identifies the language of text, but the question requires extracting answers from policy documents, not detecting their language.
★ When this WOULD be the correct answer
A company receives customer feedback in multiple languages and needs to route each message to the appropriate language-specific support team. Language Detection would be used to identify the language of each message.
Why candidates choose this
Candidates might think language detection is necessary to process multilingual documents, but the question focuses on answer extraction, not language identification.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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Azure Machine Learning Studio
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.