AI-102 Practice Question: Implement natural language processing solutions
A financial services company uses Azure AI Language to analyze customer support transcripts. They want to identify the main topics discussed in each conversation and generate a summary of the key points. The solution must minimize development effort and use prebuilt functionality. You need to recommend the appropriate Azure AI Language features. What should you use?
⚠ Common exam trap
AI-102 often tests whether candidates know which Language features are prebuilt versus custom, so they pick Custom NER or entity linking when the scenario explicitly asks for prebuilt functionality with minimal effort.
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 and conversation summarization.
Key phrase extraction identifies the main topics in text, and conversation summarization produces a summary of key points from a conversation. Both are prebuilt Azure AI Language features requiring no custom training, minimizing development effort. Together they meet the requirement to identify topics and summarize transcripts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom Named Entity Recognition (NER) and conversation summarization.
Why it's wrong here
Custom NER requires labelled training data and model building, contradicting the minimise-development-effort and prebuilt-functionality requirements. Prebuilt key phrase extraction identifies main topics without training; custom NER suits bespoke domain entity extraction where no prebuilt model exists.
- ✓
Key phrase extraction and conversation summarization.
Why this is correct
Key phrase extraction surfaces the salient terms per transcript, while conversation summarization condenses the key points. Both are prebuilt Azure AI Language capabilities, satisfying the stem's requirement to minimise development effort rather than train custom models.
- ✗
Entity linking and conversation summarization.
Why it's wrong here
Entity linking resolves mentioned entities to knowledge base entries such as Wikipedia articles; it does not surface the main topics of a conversation. Prebuilt key phrase extraction extracts salient topics directly, which is what the scenario requires.
- ✗
Sentiment analysis and key phrase extraction.
Why it's wrong here
Sentiment analysis returns positive, negative or neutral scores, and key phrase extraction alone yields topics but no summary of key points. Conversation summarization is the prebuilt feature that produces the required summary, so this pairing omits it.
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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
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.