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
What is 'question answering' in Azure AI Language and what are its two main types?
⚠ Common exam trap
A common mix-up: candidates confuse the 'two main types' with operational characteristics (e.g., real-time vs. batch) or data format distinctions (structured vs. unstructured), rather than recognizing the official Azure classification based on whether the knowledge source is pre-trained (Custom) or provided at query time (Prebuilt).
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 QA (trained on your documents) and prebuilt QA (document provided at query time)
Azure AI Language's 'question answering' feature provides two distinct capabilities: Custom QA, where you train a model on your own documents (e.g., PDFs, FAQs) to answer questions from that knowledge base, and Prebuilt QA, which uses a document provided at query time to extract answers without prior training. This distinction is fundamental to how the service is deployed—either as a persistent, trained knowledge base or as an on-the-fly extraction from a user-supplied 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.
- ✗
Multiple-choice question generation and open-ended answer scoring
Why it's wrong here
This option describes educational or assessment capabilities like automatically generating multiple-choice questions or scoring open-ended responses, which are not what Azure AI Language's question answering service does. The service extracts answer spans from a given text corpus—whether custom-trained or prebuilt—rather than generating new questions or evaluating answer quality. Therefore, this pair of functions is unrelated to the service's actual two-type categorization of custom versus prebuilt QA.
- ✓
Custom QA (trained on your documents) and prebuilt QA (document provided at query time)
Why this is correct
Custom question answering (QA) trains a knowledge base on your own documents, FAQs, and curated question-answer pairs, allowing the model to learn from that content before runtime. In contrast, prebuilt QA requires no training; you supply a document or URL directly in the API request, and the service extracts answers from that provided text. Both return precise natural language answers, but they differ fundamentally in whether the knowledge source is pre-built into the model or supplied at query time.
- ✗
Structured QA for databases and unstructured QA for text documents
Why it's wrong here
This option wrongly equates the two formats with database versus document sources. Structured QA for databases typically uses SQL or other query languages, not natural language answer extraction. Azure AI Language's question answering, both custom and prebuilt, operates on unstructured text such as FAQs or manuals—it does not directly query structured database tables—so the real distinction is custom-trained versus prebuilt document-at-query-time, not data structure.
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Real-time QA for chatbots and batch QA for scheduled document processing
Why it's wrong here
This option misclassifies the two question-answering types by processing timing. Real-time and batch are operational scheduling choices; Azure AI Language's custom question answering serves queries in real time regardless of whether the knowledge base is custom-trained or uses a prebuilt document. Custom QA and prebuilt QA differ in how knowledge is provided—training on a knowledge base versus passing a document in the request—not in when processing occurs.
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Types of AI Workloads
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.
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.
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