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 company wants to build an FAQ bot that can answer questions based on its internal knowledge base. The questions from users are often phrased in different ways. They want to match the user's intent to pre-defined answers without training a custom model. Which Azure AI Language feature should they use?
⚠ Common exam trap
Many candidates confuse Custom Question Answering with LUIS, thinking both require custom training, but Custom Question Answering uses a pre-built ranking model that works out-of-the-box with a knowledge base, while LUIS requires explicit intent and entity labeling.
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 (formerly QnA Maker) is the correct choice because it allows you to ingest a knowledge base (e.g., FAQs, manuals) and match user questions to pre-defined answers using a built-in ranking model, without training a custom ML model. It handles varied phrasing through semantic understanding and returns the best answer from the curated content, directly addressing the requirement to match intent to pre-defined answers without custom training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Custom Question Answering
Why this is correct
Custom Question Answering is a feature of Azure Cognitive Service for Language that lets you build a knowledge base from documents, FAQs, and web pages, then match user queries to the closest pre-defined QnA pair. It uses a ranking model with semantic similarity and handles varied phrasing, synonyms, and alternate forms without requiring you to train a custom ML model. This makes it the ideal service for an FAQ bot that must return answers from an existing knowledge base.
- ✗
Language Understanding (LUIS)
Why it's wrong here
Language Understanding (LUIS) is a conversational AI service that requires you to define intents, entities, and example utterances, then train and publish a custom model. Its purpose is to interpret the user's goal and extract structured data, not to retrieve static answer text from a knowledge base. Since the scenario explicitly says to avoid training a custom model, LUIS is disqualified, and even if trained, it would not efficiently serve as a FAQ answer retriever.
- ✗
Translator
Why it's wrong here
Azure Translator performs neural machine translation between languages, converting the text of a question from one language to another while preserving meaning. It has no knowledge base, no answer retrieval, and no understanding of domain-specific facts, so it cannot produce answers to questions. Using it for an FAQ bot would merely translate the user's query and provide no response content.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment analysis, part of Azure Text Analytics, evaluates text and returns a sentiment score (positive, neutral, or negative) along with confidence values, optionally with opinion mining. It is designed for tasks like brand monitoring and customer feedback analysis, not for answering questions. Because it only assesses emotional tone, it lacks any mechanism for matching a query to a pre-defined answer.
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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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