AI-102 Practice Question: Implement natural language processing solutions
Your company uses Azure AI Language to analyze customer feedback from surveys. The current pipeline extracts key phrases and sentiment. The data science team wants to identify emerging topics over time, such as new product complaints or feature requests. You need to modify the pipeline to track topic evolution. Which Azure AI Language feature should you enable?
⚠ Common exam trap
Microsoft often tests the distinction between prebuilt features (like key phrase extraction or NER) and customizable features (like custom text classification), leading candidates to choose a prebuilt option that cannot adapt to emerging, domain-specific topics.
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 text classification
Custom text classification is the correct choice because it allows you to define and train a model to categorize feedback into custom classes (e.g., 'new product complaint', 'feature request') and track their frequency over time, enabling topic evolution analysis. Unlike prebuilt features, custom classification adapts to your specific domain and can be retrained as new topics emerge, directly supporting the data science team's goal of identifying emerging topics.
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 (NER)
Why it's wrong here
NER identifies entities (people, places, organizations) but not abstract topics like product complaints or feature requests.
- ✗
Conversation summarization
Why it's wrong here
Conversation summarization is designed for summarizing multi-turn conversations, not for analyzing individual survey responses or tracking topics over time.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction extracts important terms but does not categorize them into topics or track evolution; it's not designed for topic modeling.
- ✓
Custom text classification
Why this is correct
Custom text classification allows you to define topic categories (e.g., 'pricing complaint', 'feature request') and classify each survey response. By tracking classification frequencies over time, you can identify emerging topics.
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