AI-102 Practice Question: Implement natural language processing solutions
A financial services firm uses Azure AI Language to analyze earnings call transcripts. They need to extract key phrases and identify sentiment for each speaker's turn. Which approach should they use?
⚠ Common exam trap
Many exam-takers assume the prebuilt sentiment analysis API can handle multi-speaker transcripts by default, but it processes the entire input as one document, so splitting by speaker turns is necessary for per-speaker granularity.
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
✓
Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part
The requirement is to extract key phrases and identify sentiment per speaker turn. The Azure AI Language key phrase extraction and sentiment analysis APIs operate on individual text inputs. By splitting the transcript by speaker turns, each segment can be analyzed independently, providing per-speaker insights. Processing the entire transcript as a single document would aggregate sentiment and key phrases, losing per-speaker granularity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Call the prebuilt sentiment analysis API on the entire transcript
Why it's wrong here
Prebuilt sentiment analysis does not differentiate speakers.
- ✓
Split the transcript by speaker turns and call key phrase extraction and sentiment analysis on each part
Why this is correct
Splitting by speaker turns allows per-speaker analysis.
- ✗
Use QnA Maker to extract Q&A pairs per speaker
Why it's wrong here
QnA Maker is for FAQ, not sentiment analysis.
- ✗
Use Text Analytics for health to extract entities and sentiment
Why it's wrong here
Text Analytics for health is for healthcare.
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