- 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.
AI-102 Practice Question: Implement natural language processing solutions
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 D is correct because the conversation summarization API supports utterance-level sentiment and key phrase extraction, and the orchestration workflow model routes intents across different projects. Option A is wrong because the prebuilt conversational language understanding model does not provide sentiment per utterance. Option B is wrong because custom text classification requires separate training and doesn't handle conversation context. Option C is wrong because the prebuilt key phrase extraction API does not provide sentiment analysis.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
- ✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
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
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
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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 — Static NAT maps one inside address to one outside address..
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 D is correct because the conversation summarization API supports utterance-level sentiment and key phrase extraction, and the orchestration workflow model routes intents across different projects. Option A is wrong because the prebuilt conversational language understanding model does not provide sentiment per utterance. Option B is wrong because custom text classification requires separate training and doesn't handle conversation context. Option C is wrong because the prebuilt key phrase extraction API does not provide sentiment analysis.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.
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