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.
A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?
Why wrong: Sentiment Analysis determines the positive/negative/neutral sentiment of text but does not create a knowledge base for question answering.
B
Key Phrase Extraction
Why wrong: Key Phrase Extraction identifies important phrases in text but cannot answer specific questions based on document content.
C
Custom Question Answering
Custom Question Answering allows you to build a knowledge base by ingesting documents (e.g., PDFs) and then answers questions by extracting relevant passages from that knowledge base.
D
Language Detection
Why wrong: Language Detection identifies the language in which text is written, but does not provide answers to questions.
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 (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.
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.
✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis determines the positive/negative/neutral sentiment of text but does not create a knowledge base for question answering.
✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction identifies important phrases in text but cannot answer specific questions based on document content.
✓
Custom Question Answering
Why this is correct
Custom Question Answering allows you to build a knowledge base by ingesting documents (e.g., PDFs) and then answers questions by extracting relevant passages from that knowledge base.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Detection
Why it's wrong here
Language Detection identifies the language in which text is written, but does not provide answers to questions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse general NLP features (like Key Phrase Extraction) with the specialized Q&A service, not realizing that Custom Question Answering is the only option that directly supports building a knowledge base from documents for a bot.
Trap categories for this question
Keyword trap
Key Phrase Extraction identifies important phrases in text but cannot answer specific questions based on document content.
Detailed technical explanation
How to think about this question
Custom Question Answering uses a two-step process: first, it ingests documents and extracts question-answer pairs using a machine learning model trained on FAQ-style content; second, it uses a ranker to score candidate answers based on semantic similarity to the user's query. Under the hood, it leverages Azure Cognitive Search for indexing and a transformer-based model for re-ranking, allowing it to handle paraphrased questions and return the most relevant answer snippet from the PDFs.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 Question Answering — Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.
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.
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Question Discussion
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