AI-102 Practice Question: Implement natural language processing solutions
A multinational corporation uses Azure AI Language to analyze customer feedback in multiple languages. The solution must detect the language of incoming text and then perform sentiment analysis. Which approach minimizes latency and cost?
⚠ Common exam trap
The trap is that candidates may assume the sentiment analysis API automatically detects language, but in reality it requires the language to be specified. Alternatively, they might think translation is needed, but language detection plus native sentiment analysis is more efficient for multilingual support.
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
✓
Call the language detection API followed by the sentiment analysis API
The requirement is to detect the language of incoming text and then perform sentiment analysis. The sentiment analysis API in Azure AI Language does not detect language; it requires the language to be specified. Therefore, you must first call the language detection API to identify the language, then pass that language code to the sentiment analysis API. While this involves two API calls, it is still more efficient than translating text (Option B) which adds significant latency and cost, or using cognitive skills with Azure AI Search (Option C) which adds indexing overhead. Option A is wrong because the sentiment analysis API cannot detect language on its own; it requires the language as an input parameter. Thus, Option D minimizes latency and cost by only performing the necessary steps without unnecessary transformations.
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 the sentiment analysis API with multilingual support
Why it's wrong here
The sentiment analysis API in Azure AI Language does not detect language; it requires the language code to be provided as input. Therefore, it cannot meet the requirement of detecting the language of incoming text. Using it alone would require you to already know the language, which is not the case here.
- ✗
Use the Translator service to translate text to English, then call sentiment analysis
Why it's wrong here
Adds translation cost and latency.
- ✗
Store text in Azure AI Search and use cognitive skills for sentiment
Why it's wrong here
Azure AI Search is for indexing, not real-time sentiment.
- ✓
Call the language detection API followed by the sentiment analysis API
Why this is correct
This is the correct approach because you first call the language detection API to identify the language, then call the sentiment analysis API with that language code. This combination directly meets the requirement with minimal latency and cost, avoiding translation or indexing overhead.
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