AI-102 Practice Question: Implement natural language processing solutions
You need to build a solution that extracts the main topics discussed in recorded customer service calls. The audio is already transcribed to text, and you must return the most salient phrases without any predefined categories. Which Azure AI Language feature should you use?
⚠ Common exam trap
The trap here is conflating summarization with key phrase extraction, when the requirement for short salient phrases without predefined categories points specifically to key phrase extraction.
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
✓
Key phrase extraction
Key phrase extraction is the Azure AI Language feature designed to surface the main talking points in text without any predefined categories or training. It returns a list of salient phrases per document, which can be aggregated across call transcripts to reveal discussion themes. Custom classification needs labeled classes, entity recognition targets named entities rather than topics, and extractive summarization returns sentences instead of topic phrases.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition extracts entities such as people, places, organizations, and dates, not abstract topics. A discussion about billing policies or product reliability may contain no named entities at all, so this feature would return sparse or irrelevant results for topic discovery. It answers a different question, namely who or what is mentioned, rather than what the conversation is about.
- ✗
Extractive summarization
Why it's wrong here
Extractive summarization selects the most important sentences from a document to form a summary. While that is useful for condensing a transcript, it returns sentences rather than a concise list of salient phrases or topics. The requirement is to extract main topics as phrases without predefined categories, which is the specific behavior of key phrase extraction rather than sentence selection.
- ✓
Key phrase extraction
Why this is correct
Key phrase extraction returns the main talking points from a document without requiring predefined categories. Because the transcripts have no labels and the goal is to surface salient topics, this feature matches the requirement directly. It is available through the analyze-text endpoint and returns a list of key phrases per document, which can be aggregated across calls to summarize discussion themes.
- ✗
Custom text classification
Why it's wrong here
Custom text classification requires you to define classes and label training documents before it can predict anything. The scenario explicitly states there are no predefined categories, so this feature cannot be used without first building and labeling a project. It also returns a category label rather than a list of salient phrases, so its output shape does not match the requirement.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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