AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Training custom language models for complex conversations

  Why it's wrong here

  Question answering in Azure AI Language does not train custom language models. It relies on a pre-built extractive model to select answer spans from a user-defined knowledge base. Complex conversational flows are handled by Conversational Language Understanding (CLU), which can train custom models for intent and entity detection across multi-turn dialogues. Thus, this describes CLU, not the question answering capability.

• ✓

  Building knowledge bases that automatically answer questions from FAQ content

  Why this is correct

  This is exactly what Azure AI Language's question answering feature is designed to do. It ingests FAQ-style documents, product manuals, and frequently visited URLs, then builds a structured knowledge base. When a user asks a question in natural language, the service ranks candidate answers and returns the best one, optionally with a confidence score. This capability directly supports building automated answer systems from existing FAQ content.

• ✗

  Generating new questions from a given text

  Why it's wrong here

  Generating new questions from source text is a distinct Text Analytics capability called question generation, which creates suggested questions from a passage. Question answering operates in the opposite direction: it starts with a user's question and retrieves the most relevant answer from existing FAQ content, documents, or URLs. It does not synthesize or create new questions. Thus, the two features perform inverse tasks, making this option incorrect.

• ✗

  Testing the quality of chatbot responses

  Why it's wrong here

  Testing the quality of chatbot responses is an evaluation activity, typically done using tools like Azure AI Studio's prompt evaluation or by manually comparing generated answers against ground truth. Question answering, in contrast, is a live service that returns answers from a curated knowledge base when a user submits a natural language question. It does not include a built-in quality-testing workflow for chatbot responses. Therefore, this option confuses a development-time process with a runtime inference feature.

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