- A
Multiple-choice question generation and open-ended answer scoring
Why wrong: Question generation and scoring are educational AI features — Azure AI Language's QA extracts answers from documents.
- B
Custom QA (trained on your documents) and prebuilt QA (document provided at query time)
Custom QA trains on your knowledge base; prebuilt QA queries a document provided in the request — both extract natural language answers.
- C
Structured QA for databases and unstructured QA for text documents
Why wrong: Database querying is SQL — Azure AI Language's QA focuses on natural language understanding of text documents.
- D
Real-time QA for chatbots and batch QA for scheduled document processing
Why wrong: Processing timing is an operational concern — the two QA types differ in how knowledge is provided, not when processing occurs.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'question answering' in Azure AI Language and what are its two main types?
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)
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Question generation and scoring are educational AI features — Azure AI Language's QA extracts answers from documents.
- ✓
Custom QA (trained on your documents) and prebuilt QA (document provided at query time)
Why this is correct
Custom QA trains on your knowledge base; prebuilt QA queries a document provided in the request — both extract natural language answers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Structured QA for databases and unstructured QA for text documents
Why it's wrong here
Database querying is SQL — Azure AI Language's QA focuses on natural language understanding of text documents.
- ✗
Real-time QA for chatbots and batch QA for scheduled document processing
Why it's wrong here
Processing timing is an operational concern — the two QA types differ in how knowledge is provided, not when processing occurs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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).
Detailed technical explanation
How to think about this question
Under the hood, Custom QA uses a trained model that indexes your documents into a knowledge base using Azure Cognitive Search, enabling fast retrieval of answers via a confidence-scored ranking algorithm. Prebuilt QA leverages a generalized model that processes a document provided in the request (e.g., via a URL or text input) and extracts answers using natural language understanding without any prior training on that specific content. A real-world scenario is a customer support chatbot that uses Custom QA for its FAQ knowledge base but falls back to Prebuilt QA when a user uploads a new product manual for immediate querying.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Custom QA (trained on your documents) and prebuilt QA (document provided at query time) — Option B is correct because 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 11, 2026
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