AI-102 Practice Question: Implement natural language processing solutions
You are building a solution that uses Azure AI Language to analyze transcribed call-center conversations. The transcripts are stored as plain text in Azure Blob Storage. You need to identify the specific products, dates, and monetary amounts mentioned in each conversation while distinguishing them from generic nouns. You also need to return the character offset and length for each detected mention so the UI can highlight them. Which Azure AI Language feature should you use?
⚠ Common exam trap
The trap here is assuming any phrase extraction feature returns entity categories and offsets, when only NER provides typed entities with span metadata.
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
✓
Named Entity Recognition (NER)
Named Entity Recognition in Azure AI Language is designed to detect entity categories such as Product, DateTime, and Quantity and to return each mention with its offset and length. Those offsets allow the application to highlight exact spans in the transcript, and the category labels let developers filter products from dates and amounts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns salient phrases but does not assign entity categories and does not reliably separate dates or monetary amounts from other key phrases. It also does not guarantee offset and length values in the same structured way needed for precise UI highlighting, so it cannot satisfy the product, date, and money distinction requirement.
- ✓
Named Entity Recognition (NER)
Why this is correct
NER returns entity categories such as Product, DateTime, and Quantity with the exact text, offset, and length for each mention, which supports highlighting in the UI. It distinguishes specific entity types from generic tokens, matching the requirement to isolate products, dates, and money amounts from ordinary nouns.
- ✗
Extractive Summarization
Why it's wrong here
Extractive Summarization selects the most important sentences from a document; it does not identify individual named entities or provide character offsets for each mention. It would produce a condensed transcript rather than a list of products, dates, and monetary amounts, so it fails the highlighting and entity-type requirement.
- ✗
Conversation Summarization
Why it's wrong here
Conversation Summarization produces abstractive issue and resolution summaries for chat or call transcripts. It does not extract fine-grained entities like Product or Quantity and does not return per-mention offsets or lengths, so it cannot drive the UI highlighting requirement even though it targets conversational data.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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