Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A travel agency wants to build a chatbot that can automatically answer customer questions about flight status by extracting answers from a PDF document containing FAQs. Which Azure AI Language feature should they use to directly query this content?
⚠ Common exam trap
It's easy for candidates to confuse Conversational Language Understanding (CLU) with Question Answering, mistakenly thinking that CLU can directly answer from a document, when in fact CLU requires explicit training on intents and entities and does not perform document-based extractive QA.
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
✓
Question Answering
Question Answering, is correct because it is specifically designed to extract answers directly from a provided document (such as a PDF FAQ) by using a pre-built or custom knowledge base. The travel agency can upload the PDF, and the service will return precise answers to user queries without requiring intent classification or entity extraction, which is exactly what is needed for querying flight status from a static FAQ document.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Conversational Language Understanding (CLU)
Why it's wrong here
Conversational Language Understanding (CLU) is designed to classify the user's overall intent (for example, 'BookFlight' or 'CheckWeather') and extract named entities (like dates or cities) from natural-language utterances. It does not read or index a document, and it has no retrieval or reading-comprehension component that could locate a relevant passage inside an FAQ text. While a chatbot could use CLU to route a user to a fallback, CLU alone cannot generate answers grounded in a source document, making it insufficient for automatically answering from a knowledge base.
When this WOULD be correct
If the travel agency wanted to build a chatbot that understands free-form customer queries and maps them to specific intents (e.g., 'book flight', 'cancel reservation') and extracts entities (e.g., dates, destinations), then CLU would be the correct choice.
- ✓
Question Answering
Why this is correct
Question Answering is the Azure AI Language feature purpose-built to consume semi-structured content such as FAQ pages, manuals, or brochures, index it into a knowledge base, and respond to user queries by returning the most relevant passage or answer span. It uses a two-stage retrieval-and-reading pipeline: a search engine first retrieves candidate documents, then a machine-reading comprehension model extracts the exact text that answers the question. Because the travel agency's requirement is to automatically answer from existing documents, this service provides the exact mechanism needed, including built-in confidence scoring, active learning, and multi-turn conversation support.
- ✗
Text Analytics for health
Why it's wrong here
Text Analytics for health is a specialized Azure service that applies pre-trained medical natural-language-processing models to extract clinical concepts such as diagnoses, medications, procedures, and their relations from unstructured medical text. It is domain-restricted to healthcare terminology and expects clinical input; it has no general-purpose question-answering capability and cannot be pointed at a travel FAQ document to produce travel-related answers. Using it for a travel agency would be both technically mismatched and out of scope, as the model is not trainable or adaptable to non-clinical domains.
When this WOULD be correct
A healthcare organization needs to extract medication names, diagnoses, and treatment details from clinical notes for a patient summary. Text Analytics for health would be the correct choice because it specializes in recognizing medical entities and their relationships.
- ✗
Translator
Why it's wrong here
Translator is a cloud-based machine translation service that converts text or documents from one language to another, such as English to Spanish, while preserving the original meaning as closely as possible. It performs no semantic understanding beyond translation and contains no knowledge base, no document-retrieval mechanism, and no question-answering model. Even though a travel agency might use Translator to localize the chatbot's responses after the answer has been found, Translator cannot fulfill the core requirement of extracting an answer directly from a source document.
When this WOULD be correct
A travel agency needs to translate customer queries from English to Spanish before processing them with a FAQ chatbot. Translator would be the correct choice to enable multilingual support.
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.
✓Question AnsweringCorrect answer▾
Why this is correct
Question Answering is the Azure AI Language feature purpose-built to consume semi-structured content such as FAQ pages, manuals, or brochures, index it into a knowledge base, and respond to user queries by returning the most relevant passage or answer span. It uses a two-stage retrieval-and-reading pipeline: a search engine first retrieves candidate documents, then a machine-reading comprehension model extracts the exact text that answers the question. Because the travel agency's requirement is to automatically answer from existing documents, this service provides the exact mechanism needed, including built-in confidence scoring, active learning, and multi-turn conversation support.
✗Conversational Language Understanding (CLU)Wrong answer — click to see why▾
Why this is wrong here
CLU is designed for intent classification and entity extraction from conversational utterances, not for extracting answers directly from a PDF document. The question requires querying a static FAQ document, which is the domain of Question Answering.
★ When this WOULD be the correct answer
If the travel agency wanted to build a chatbot that understands free-form customer queries and maps them to specific intents (e.g., 'book flight', 'cancel reservation') and extracts entities (e.g., dates, destinations), then CLU would be the correct choice.
Why candidates choose this
Candidates may confuse CLU's conversational capabilities with the ability to answer questions from documents, not realizing that CLU requires structured intents and entities rather than direct document querying.
✗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 FAQ documents. The question requires querying a PDF for direct answers, which is the domain of Question Answering.
★ When this WOULD be the correct answer
A healthcare organization needs to extract medication names, diagnoses, and treatment details from clinical notes for a patient summary. Text Analytics for health would be the correct choice because it specializes in recognizing medical entities and their relationships.
Why candidates choose this
Candidates may see 'extracting answers from a document' and associate it with text analytics features, overlooking that Text Analytics for health is narrowly focused on healthcare data, not general FAQ querying.
✗TranslatorWrong answer — click to see why▾
Why this is wrong here
The question requires extracting answers from a PDF document, which is a static text source. Translator is designed for language translation, not for querying or extracting information from documents.
★ When this WOULD be the correct answer
A travel agency needs to translate customer queries from English to Spanish before processing them with a FAQ chatbot. Translator would be the correct choice to enable multilingual support.
Why candidates choose this
Candidates might think that since the chatbot interacts with customers, translation could be needed for multilingual support, but the question specifically asks about querying a PDF document, not translating languages.
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
Regression and Classification
Key term
Entity extraction
Entity extraction is the process of automatically identifying and classifying named entities in text, such as people, organizations, locations, dates, and technical terms, turning unstructured data into structured information.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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