- A
Use custom text classification to classify each utterance into intent categories and use the sentiment analysis API on the entire transcript.
Why wrong: Custom text classification works on whole documents, not per utterance; also sentiment on the entire transcript loses per-utterance granularity.
- B
Use the prebuilt key phrase extraction API to identify important terms and the prebuilt sentiment analysis API for overall transcript sentiment, then map intents via a rules-based approach.
Why wrong: Key phrase extraction does not detect intents; a rules-based approach is brittle and not scalable for multiple intents.
- C
Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection.
Conversation summarization provides utterance-level sentiment and key phrases; orchestration workflow allows routing to a custom CLU project for intent detection, handling multi-turn conversations effectively.
- D
Use the prebuilt conversational language understanding model for intent detection and Azure AI Language sentiment analysis API for utterance-level sentiment, processing each utterance independently via Azure Functions.
Why wrong: The prebuilt CLU model is designed for common intents but does not provide sentiment per utterance; you would need a custom model for sentiment.
Real-Time Analysis of Customer Call Transcripts with Azure AI Language
This AI-102 practice question tests your understanding of implement natural language processing solutions. 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.
You are designing a solution to analyze customer call transcripts using Azure AI Language. The solution must extract key phrases, detect sentiment per utterance, and identify the customer's intent (e.g., 'cancel subscription', 'technical support'). The data is stored in Azure Blob Storage and processed in near real-time. Which combination of Azure AI Language features and processing pattern should you use?
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
Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection.
Option C is correct because it combines the conversation summarization API (which provides utterance-level sentiment and key phrase extraction) with an orchestration workflow model that routes to a custom conversational language understanding (CLU) project for intent detection. This pattern supports near real-time processing of call transcripts from Azure Blob Storage, meeting the requirements for per-utterance sentiment, key phrase extraction, and intent identification.
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.
- ✗
Use custom text classification to classify each utterance into intent categories and use the sentiment analysis API on the entire transcript.
Why it's wrong here
Custom text classification works on whole documents, not per utterance; also sentiment on the entire transcript loses per-utterance granularity.
- ✗
Use the prebuilt key phrase extraction API to identify important terms and the prebuilt sentiment analysis API for overall transcript sentiment, then map intents via a rules-based approach.
Why it's wrong here
Key phrase extraction does not detect intents; a rules-based approach is brittle and not scalable for multiple intents.
- ✓
Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection.
Why this is correct
Conversation summarization provides utterance-level sentiment and key phrases; orchestration workflow allows routing to a custom CLU project for intent detection, handling multi-turn conversations effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use the prebuilt conversational language understanding model for intent detection and Azure AI Language sentiment analysis API for utterance-level sentiment, processing each utterance independently via Azure Functions.
Why it's wrong here
The prebuilt CLU model is designed for common intents but does not provide sentiment per utterance; you would need a custom model for sentiment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume prebuilt models (like CLU or sentiment analysis) are sufficient for custom intents and utterance-level analysis, overlooking the need for orchestration and custom training to handle domain-specific requirements.
Trap categories for this question
Keyword trap
Key phrase extraction does not detect intents; a rules-based approach is brittle and not scalable for multiple intents.
Detailed technical explanation
How to think about this question
The conversation summarization API in Azure AI Language can extract utterance-level sentiment and key phrases by analyzing the dialogue structure, including speaker roles (e.g., agent vs. customer). The orchestration workflow model allows you to route utterances to a custom CLU project, which uses a trained model to map natural language to specific intents and entities, enabling accurate intent detection even with varied phrasing. This pattern is ideal for near real-time scenarios because it leverages asynchronous processing and can be triggered by Blob Storage events via Azure Functions or Event Grid.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the conversation summarization API (with utterance-level sentiment and key phrase extraction) and an orchestration workflow model that routes to a custom conversational language understanding project for intent detection. — Option C is correct because it combines the conversation summarization API (which provides utterance-level sentiment and key phrase extraction) with an orchestration workflow model that routes to a custom conversational language understanding (CLU) project for intent detection. This pattern supports near real-time processing of call transcripts from Azure Blob Storage, meeting the requirements for per-utterance sentiment, key phrase extraction, and intent identification.
What should I do if I get this AI-102 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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Last reviewed: Jul 4, 2026
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