Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A customer support team wants to create a chatbot that can answer common questions about employee benefits. They have a PDF document containing a list of frequently asked questions with their answers. Which Azure AI Language feature should they use to build a solution that extracts answers directly from this content?
⚠ Common exam trap
It's easy for candidates to confuse Key Phrase Extraction (B) with question answering, not realizing that Key Phrase Extraction only identifies terms without providing direct answers, while Custom Question Answering is the only feature that returns extracted answers from a document.
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 (C) is the correct choice because it is specifically designed to ingest documents like PDFs and extract question-answer pairs from them, enabling a chatbot to respond directly with answers from the content. This feature uses a pre-built or custom knowledge base to match user queries to the most relevant answer, making it ideal for the described scenario.
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, another Azure AI Text Analytics feature, assigns a polarity label (positive, negative, neutral, or mixed) to text and provides confidence scores for each label at the document and sentence level. It is designed for opinion mining and social-media monitoring, not for answer retrieval. A chatbot using it could only infer whether the customer is frustrated or satisfied, not respond with the correct answer from a FAQ.
When this WOULD be correct
A company wants to analyze customer feedback to determine if overall sentiment is positive, negative, or neutral. Sentiment Analysis would be the correct Azure AI Language feature for that task.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction, a capability of Azure AI Text Analytics, scans a document and returns the nouns and noun-like phrases that are statistically most significant, such as 'refund policy' or 'shipping fees'. It is purely a lexical/statistical feature: it does not parse the user's intent, map a question to an answer, or maintain any kind of conversational context. Therefore, while it might surface topics, it cannot generate or retrieve an answer to a support question.
When this WOULD be correct
A scenario where the team needs to analyze customer feedback to identify the most frequently mentioned topics or issues, such as extracting key phrases from survey responses to summarize common complaints.
- ✓
Custom Question Answering
Why this is correct
Custom Question Answering (part of Azure AI Language) lets you build a knowledge base from structured content such as FAQ pages, product manuals, or support documents, then provides a runtime endpoint that matches user queries to the most relevant question-answer pairs. It uses transformer-based semantic ranking to evaluate candidate answers and returns the highest-confidence pair, optionally with follow-up prompts. This is the only service listed that is designed to actually answer questions from a curated corpus.
- ✗
Text Analytics for Health
Why it's wrong here
Text Analytics for Health is a specialized Azure AI service that extracts medical entities (e.g., medications, diagnoses, symptoms) and their relationships from unstructured clinical documents using domain-specific models. It lacks any general-purpose knowledge base or question-answering capability, and its use case is limited to healthcare data such as clinical notes and discharge summaries. As such, it would be entirely inappropriate for a general customer-support chatbot.
When this WOULD be correct
A healthcare organization needs to extract medication names, diagnoses, and treatment details from clinical notes to populate a structured database. Text Analytics for Health would be the correct feature for this task.
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 (part of Azure AI Language) lets you build a knowledge base from structured content such as FAQ pages, product manuals, or support documents, then provides a runtime endpoint that matches user queries to the most relevant question-answer pairs. It uses transformer-based semantic ranking to evaluate candidate answers and returns the highest-confidence pair, optionally with follow-up prompts. This is the only service listed that is designed to actually answer questions from a curated corpus.
✗Sentiment AnalysisWrong answer — click to see why▾
Why this is wrong here
Sentiment Analysis determines the emotional tone of text, but it cannot extract answers from a PDF document. The question requires extracting answers from a FAQ document, which is a task for Custom Question Answering.
★ When this WOULD be the correct answer
A company wants to analyze customer feedback to determine if overall sentiment is positive, negative, or neutral. Sentiment Analysis would be the correct Azure AI Language feature for that task.
Why candidates choose this
Candidates may confuse Sentiment Analysis with general text understanding, thinking it can interpret and extract information from documents, but it only evaluates emotional polarity.
✗Key Phrase ExtractionWrong answer — click to see why▾
Why this is wrong here
Key Phrase Extraction identifies important terms but does not extract question-answer pairs from a PDF; it lacks the ability to understand and retrieve specific answers based on questions.
★ When this WOULD be the correct answer
A scenario where the team needs to analyze customer feedback to identify the most frequently mentioned topics or issues, such as extracting key phrases from survey responses to summarize common complaints.
Why candidates choose this
Candidates may confuse extracting key phrases with extracting answers, assuming that identifying important words is sufficient for answering questions.
✗Text Analytics for HealthWrong answer — click to see why▾
Why this is wrong here
Text Analytics for Health is designed to extract medical entities and relationships from unstructured clinical text, not to answer questions from a FAQ PDF about employee benefits.
★ When this WOULD be the correct answer
A healthcare organization needs to extract medication names, diagnoses, and treatment details from clinical notes to populate a structured database. Text Analytics for Health would be the correct feature for this task.
Why candidates choose this
Candidates may see 'Text Analytics' and assume it can handle any text-based extraction, overlooking that this specific service is specialized for healthcare domains.
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?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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.
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